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Duplicate detection is the task of identifying all groups of records within a data set that represent the same real-world entity, respectively. This task is difficult, because (i) representations might differ slightly, so some similarity measure must be defined to compare pairs of records and (ii) data sets might have a high volume making a pair-wise comparison of all records infeasible. To tackle the second problem, many algorithms have been suggested that partition the data set and compare all record pairs only within each partition. One well-known such approach is the Sorted Neighborhood Method (SNM), which sorts the data according to some key and then advances a window over the data comparing only records that appear within the same window. We propose several variations of SNM that have in common a varying window size and advancement. The general intuition of such adaptive windows is that there might be regions of high similarity suggesting a larger window size and regions of lower similarity suggesting a smaller window size. We propose and thoroughly evaluate several adaption strategies, some of which are provably better than the original SNM in terms of efficiency (same results with fewer comparisons).
Unique column combinations of a relational database table are sets of columns that contain only unique values. Discovering such combinations is a fundamental research problem and has many different data management and knowledge discovery applications. Existing discovery algorithms are either brute force or have a high memory load and can thus be applied only to small datasets or samples. In this paper, the wellknown GORDIAN algorithm and "Apriori-based" algorithms are compared and analyzed for further optimization. We greatly improve the Apriori algorithms through efficient candidate generation and statistics-based pruning methods. A hybrid solution HCAGORDIAN combines the advantages of GORDIAN and our new algorithm HCA, and it significantly outperforms all previous work in many situations.
This vision article outlines the main building blocks of what we term AI Compliance, an effort to bridge two complementary research areas: computer science and the law.
Such research has the goal to model, measure, and affect the quality of AI artifacts, such as data, models, and applications, to then facilitate adherence to legal standards.
Axionlike particles (ALPs) are hypothetical light (sub-eV) bosons predicted in some extensions of the Standard Model of particle physics. In astrophysical environments comprising high-energy gamma rays and turbulent magnetic fields, the existence of ALPs can modify the energy spectrum of the gamma rays for a sufficiently large coupling between ALPs and photons. This modification would take the form of an irregular behavior of the energy spectrum in a limited energy range. Data from the H. E. S. S. observations of the distant BL Lac object PKS 2155 - 304 (z = 0.116) are used to derive upper limits at the 95% C. L. on the strength of the ALP coupling to photons, g(gamma a) < 2.1 x 10(-11) GeV-1 for an ALP mass between 15 and 60 neV. The results depend on assumptions on the magnetic field around the source, which are chosen conservatively. The derived constraints apply to both light pseudoscalar and scalar bosons that couple to the electromagnetic field.
Data dependencies, or integrity constraints, are used to improve the quality of a database schema, to optimize queries, and to ensure consistency in a database. In the last years conditional dependencies have been introduced to analyze and improve data quality. In short, a conditional dependency is a dependency with a limited scope defined by conditions over one or more attributes. Only the matching part of the instance must adhere to the dependency. In this paper we focus on conditional inclusion dependencies (CINDs). We generalize the definition of CINDs, distinguishing covering and completeness conditions. We present a new use case for such CINDs showing their value for solving complex data quality tasks. Further, we define quality measures for conditions inspired by precision and recall. We propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. Our algorithms choose not only the condition values but also the condition attributes automatically. Finally, we show that our approach efficiently provides meaningful and helpful results for our use case.
CurEx
(2018)
The integration of diverse structured and unstructured information sources into a unified, domain-specific knowledge base is an important task in many areas. A well-maintained knowledge base enables data analysis in complex scenarios, such as risk analysis in the financial sector or investigating large data leaks, such as the Paradise or Panama papers. Both the creation of such knowledge bases, as well as their continuous maintenance and curation involves many complex tasks and considerable manual effort. With CurEx, we present a modular system that allows structured and unstructured data sources to be integrated into a domain-specific knowledge base. In particular, we (i) enable the incremental improvement of each individual integration component; (ii) enable the selective generation of multiple knowledge graphs from the information contained in the knowledge base; and (iii) provide two distinct user interfaces tailored to the needs of data engineers and end-users respectively. The former has curation capabilities and controls the integration process, whereas the latter focuses on the exploration of the generated knowledge graph.
Effective query optimization is a core feature of any database management system. While most query optimization techniques make use of simple metadata, such as cardinalities and other basic statistics, other optimization techniques are based on more advanced metadata including data dependencies, such as functional, uniqueness, order, or inclusion dependencies. This survey provides an overview, intuitive descriptions, and classifications of query optimization and execution strategies that are enabled by data dependencies. We consider the most popular types of data dependencies and focus on optimization strategies that target the optimization of relational database queries. The survey supports database vendors to identify optimization opportunities as well as DBMS researchers to find related work and open research questions.
Data Preparation
(2020)
Raw data are often messy: they follow different encodings, records are not well structured, values do not adhere to patterns, etc. Such data are in general not fit to be ingested by downstream applications, such as data analytics tools, or even by data management systems. The act of obtaining information from raw data relies on some data preparation process. Data preparation is integral to advanced data analysis and data management, not only for data science but for any data-driven applications. Existing data preparation tools are operational and useful, but there is still room for improvement and optimization. With increasing data volume and its messy nature, the demand for prepared data increases day by day. <br /> To cater to this demand, companies and researchers are developing techniques and tools for data preparation. To better understand the available data preparation systems, we have conducted a survey to investigate (1) prominent data preparation tools, (2) distinctive tool features, (3) the need for preliminary data processing even for these tools and, (4) features and abilities that are still lacking. We conclude with an argument in support of automatic and intelligent data preparation beyond traditional and simplistic techniques.
Data errors represent a major issue in most application workflows. Before any important task can take place, a certain data quality has to be guaranteed by eliminating a number of different errors that may appear in data. Typically, most of these errors are fixed with data preparation methods, such as whitespace removal. However, the particular error of duplicate records, where multiple records refer to the same entity, is usually eliminated independently with specialized techniques. Our work is the first to bring these two areas together by applying data preparation operations under a systematic approach prior to performing duplicate detection. <br /> Our process workflow can be summarized as follows: It begins with the user providing as input a sample of the gold standard, the actual dataset, and optionally some constraints to domain-specific data preparations, such as address normalization. The preparation selection operates in two consecutive phases. First, to vastly reduce the search space of ineffective data preparations, decisions are made based on the improvement or worsening of pair similarities. Second, using the remaining data preparations an iterative leave-one-out classification process removes preparations one by one and determines the redundant preparations based on the achieved area under the precision-recall curve (AUC-PR). Using this workflow, we manage to improve the results of duplicate detection up to 19% in AUC-PR.
Ground-based gamma-ray astronomy has had a major breakthrough with the impressive results obtained using systems of imaging atmospheric Cherenkov telescopes. Ground-based gamma-ray astronomy has a huge potential in astrophysics, particle physics and cosmology. CTA is an international initiative to build the next generation instrument, with a factor of 5-10 improvement in sensitivity in the 100 GeV-10 TeV range and the extension to energies well below 100 GeV and above 100 TeV. CTA will consist of two arrays (one in the north, one in the south) for full sky coverage and will be operated as open observatory. The design of CTA is based on currently available technology. This document reports on the status and presents the major design concepts of CTA.
Spreadsheets are among the most commonly used file formats for data management, distribution, and analysis. Their widespread employment makes it easy to gather large collections of data, but their flexible canvas-based structure makes automated analysis difficult without heavy preparation. One of the common problems that practitioners face is the presence of multiple, independent regions in a single spreadsheet, possibly separated by repeated empty cells. We define such files as "multiregion" files. In collections of various spreadsheets, we can observe that some share the same layout. We present the Mondrian approach to automatically identify layout templates across multiple files and systematically extract the corresponding regions. Our approach is composed of three phases: first, each file is rendered as an image and inspected for elements that could form regions; then, using a clustering algorithm, the identified elements are grouped to form regions; finally, every file layout is represented as a graph and compared with others to find layout templates. We compare our method to state-of-the-art table recognition algorithms on two corpora of real-world enterprise spreadsheets. Our approach shows the best performances in detecting reliable region boundaries within each file and can correctly identify recurring layouts across files.
With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (fds). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through fds, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed fds (rfds) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose Domino, a new discovery algorithm for rfds that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring rfds. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm.
Functional dependencies (FDs) play an important role in maintaining data quality. They can be used to enforce data consistency and to guide repairs over a database. In this work, we investigate the problem of missing values and its impact on FD discovery. When using existing FD discovery algorithms, some genuine FDs could not be detected precisely due to missing values or some non-genuine FDs can be discovered even though they are caused by missing values with a certain NULL semantics. We define a notion of genuineness and propose algorithms to compute the genuineness score of a discovered FD. This can be used to identify the genuine FDs among the set of all valid dependencies that hold on the data. We evaluate the quality of our method over various real-world and semi-synthetic datasets with extensive experiments. The results show that our method performs well for relatively large FD sets and is able to accurately capture genuine FDs.
Discovery of high and very high-energy emission from the BL Lacertae object SHBL J001355.9-185406
(2013)
The detection of the high-frequency peaked BL Lac object (HBL) SHBL J001355.9-185406 (z = 0.095) at high (HE; 100 MeV < E < 300 GeV) and very high-energy (VHE; E > 100 GeV) with the Fermi Large Area Telescope (LAT) and the High Energy Stereoscopic System (H.E.S.S.) is reported. Dedicated observations were performed with the H. E. S. S. telescopes, leading to a detection at the 5.5 sigma significance level. The measured flux above 310 GeV is (8.3 +/- 1.7(stat) +/- 1.7(sys)) x 10(-13) photons cm(-2) s(-1) (about 0.6% of that of the Crab Nebula), and the power-law spectrum has a photon index of Gamma = 3.4 +/- 0.5(stat) +/- 0.2(sys). Using 3.5 years of publicly available Fermi-LAT data, a faint counterpart has been detected in the LAT data at the 5.5 sigma significance level, with an integrated flux above 300 MeV of (9.3 +/- 3.4(stat) +/- 0.8(sys)) x 10(-10) photons cm(-2) s(-1) and a photon index of Gamma = 1.96 +/- 0.20(stat) +/- 0.08(sys). X-ray observations with Swift-XRT allow the synchrotron peak energy in vF(v) representation to be located at similar to 1.0 keV. The broadband spectral energy distribution is modelled with a one-zone synchrotron self-Compton (SSC) model and the optical data by a black-body emission describing the thermal emission of the host galaxy. The derived parameters are typical of HBLs detected at VHE, with a particle-dominated jet.
Discovery of very high energy gamma-ray emission from the BL Lacertae
object PKS0301-243 with HESS
(2013)
The active galactic nucleus PKS 0301-243 (z = 0.266) is a high-synchrotron-peaked BL Lac object that is detected at high energies (HE, 100 MeV < E < 100 GeV) by Fermi/LAT. This paper reports on the discovery of PKS 0301-243 at very high energies (E > 100 GeV) by the High Energy Stereoscopic System (H.E.S.S.) from observations between September 2009 and December 2011 for a total live time of 34.9 h. Gamma rays above 200 GeV are detected at a significance of 9.4 sigma. A hint of variability at the 2.5 sigma level is found. An integral flux I(E > 200GeV) = (3.3 +/- 1.1(stat) +/- 0.7(syst)) x 10(-12) ph cm(-2) s(-1) and a photon index Gamma = 4.6 +/- 0.7(stat) +/- 0.2(syst) are measured. Multi-wavelength light curves in HE, X-ray and optical bands show strong variability, and a minimal variability timescale of eight days is estimated from the optical light curve. A single-zone leptonic synchrotron self-Compton scenario satisfactorily reproduces the multi-wavelength data. In this model, the emitting region is out of equipartition and the jet is particle dominated. Because of its high redshift compared to other sources observed at TeV energies, the very high energy emission from PKS 0301-243 is attenuated by the extragalactic background light (EBL) and the measured spectrum is used to derive an upper limit on the opacity of the EBL.
Data obtained from foreign data sources often come with only superficial structural information, such as relation names and attribute names. Other types of metadata that are important for effective integration and meaningful querying of such data sets are missing. In particular, relationships among attributes, such as foreign keys, are crucial metadata for understanding the structure of an unknown database. The discovery of such relationships is difficult, because in principle for each pair of attributes in the database each pair of data values must be compared. A precondition for a foreign key is an inclusion dependency (IND) between the key and the foreign key attributes. We present with Spider an algorithm that efficiently finds all INDs in a given relational database. It leverages the sorting facilities of DBMS but performs the actual comparisons outside of the database to save computation. Spider analyzes very large databases up to an order of magnitude faster than previous approaches. We also evaluate in detail the effectiveness of several heuristics to reduce the number of necessary comparisons. Furthermore, we generalize Spider to find composite INDs covering multiple attributes, and partial INDs, which are true INDs for all but a certain number of values. This last type is particularly relevant when integrating dirty data as is often the case in the life sciences domain - our driving motivation.
Matching dependencies (MDs) are data profiling results that are often used for data integration, data cleaning, and entity matching. They are a generalization of functional dependencies (FDs) matching similar rather than same elements. As their discovery is very difficult, existing profiling algorithms find either only small subsets of all MDs or their scope is limited to only small datasets.
We focus on the efficient discovery of all interesting MDs in real-world datasets. For this purpose, we propose HyMD, a novel MD discovery algorithm that finds all minimal, non-trivial MDs within given similarity boundaries. The algorithm extracts the exact similarity thresholds for the individual MDs from the data instead of using predefined similarity thresholds. For this reason, it is the first approach to solve the MD discovery problem in an exact and truly complete way. If needed, the algorithm can, however, enforce certain properties on the reported MDs, such as disjointness and minimum support, to focus the discovery on such results that are actually required by downstream use cases. HyMD is technically a hybrid approach that combines the two most popular dependency discovery strategies in related work: lattice traversal and inference from record pairs. Despite the additional effort of finding exact similarity thresholds for all MD candidates, the algorithm is still able to efficiently process large datasets, e.g., datasets larger than 3 GB.
Entity resolution on-demand
(2022)
Entity Resolution (ER) aims to identify and merge records that refer to the same real-world entity.
ER is typically employed as an expensive cleaning step on the entire data before consuming it. Yet, determining which entities are useful once cleaned depends solely on the user's application, which may need only a fraction of them.
For instance, when dealing with Web data, we would like to be able to filter the entities of interest gathered from multiple sources without cleaning the entire, continuously-growing data.
Similarly, when querying data lakes, we want to transform data on-demand and return the results in a timely manner-a fundamental requirement of ELT (Extract-Load-Transform) pipelines.
We propose BrewER, a framework to evaluate SQL SP queries on dirty data while progressively returning results as if they were issued on cleaned data. BrewER tries to focus the cleaning effort on one entity at a time, following an ORDER BY predicate. Thus, it inherently supports top-k and stop-and-resume execution.
For a wide range of applications, a significant amount of resources can be saved. We exhaustively evaluate and show the efficacy of BrewER on four real-world datasets.
Given a query record, record matching is the problem of finding database records that represent the same real-world object. In the easiest scenario, a database record is completely identical to the query. However, in most cases, problems do arise, for instance, as a result of data errors or data integrated from multiple sources or received from restrictive form fields. These problems are usually difficult, because they require a variety of actions, including field segmentation, decoding of values, and similarity comparisons, each requiring some domain knowledge. In this article, we study the problem of matching records that contain address information, including attributes such as Street-address and City. To facilitate this matching process, we propose a domain-specific procedure to, first, enrich each record with a more complete representation of the address information through geocoding and reverse-geocoding and, second, to select the best similarity measure per each address attribute that will finally help the classifier to achieve the best f-measure. We report on our experience in selecting geocoding services and discovering similarity measures for a concrete but common industry use-case.
This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for the use of ML models. To this effect, we conduct two legal case studies, in medical and corporate merger applications of ML. As a second contribution, we discuss the (legally required) trade-off between accuracy and explainability and demonstrate the effect in a technical case study in the context of spam classification.
Exploring Change
(2018)
Data and metadata in datasets experience many different kinds of change. Values axe inserted, deleted or updated; rows appear and disappear; columns are added or repurposed, etc. In such a dynamic situation, users might have many questions related to changes in the dataset, for instance which parts of the data are trustworthy and which are not? Users will wonder: How many changes have there been in the recent minutes, days or years? What kind of changes were made at which points of time? How dirty is the data? Is data cleansing required? The fact that data changed can hint at different hidden processes or agendas: a frequently crowd-updated city name may be controversial; a person whose name has been recently changed may be the target of vandalism; and so on. We show various use cases that benefit from recognizing and exploring such change. We envision a system and methods to interactively explore such change, addressing the variability dimension of big data challenges. To this end, we propose a model to capture change and the process of exploring dynamic data to identify salient changes. We provide exploration primitives along with motivational examples and measures for the volatility of data. We identify technical challenges that need to be addressed to make our vision a reality, and propose directions of future work for the data management community.
Roughly every third Wikipedia article contains an infobox - a table that displays important facts about the subject in attribute-value form. The schema of an infobox, i.e., the attributes that can be expressed for a concept, is defined by an infobox template. Often, authors do not specify all template attributes, resulting in incomplete infoboxes. With iPopulator, we introduce a system that automatically populates infoboxes of Wikipedia articles by extracting attribute values from the article's text. In contrast to prior work, iPopulator detects and exploits the structure of attribute values for independently extracting value parts. We have tested iPopulator on the entire set of infobox templates and provide a detailed analysis of its effectiveness. For instance, we achieve an average extraction precision of 91% for 1,727 distinct infobox template attributes.
Context. About 40% of the observation time of the High Energy Stereoscopic System (H.E.S.S.) is dedicated to studying active galactic nuclei (AGN), with the aim of increasing the sample of known extragalactic very-high-energy (VHE, E > 100 GeV) sources and constraining the physical processes at play in potential emitters.
Aims. H.E.S.S. observations of AGN, spanning a period from April 2004 to December 2011, are investigated to constrain their gamma-ray fluxes. Only the 47 sources without significant excess detected at the position of the targets are presented.
Methods. Upper limits on VHE fluxes of the targets were computed and a search for variability was performed on the nightly time scale.
Results. For 41 objects, the flux upper limits we derived are the most constraining reported to date. These constraints at VHE are compared with the flux level expected from extrapolations of Fermi-LAT measurements in the two-year catalog of AGN. The H.E.S.S. upper limits are at least a factor of two lower than the extrapolated Fermi-LAT fluxes for 11 objects Taking into account the attenuation by the extragalactic background light reduces the tension for all but two of them, suggesting intrinsic curvature in the high-energy spectra of these two AGN.
Conclusions. Compilation efforts led by current VHE instruments are of critical importance for target-selection strategies before the advent of the Cherenkov Telescope Array (CTA).
"Bad" data has a direct impact on 88% of companies, with the average company losing 12% of its revenue due to it.
Duplicates - multiple but different representations of the same real-world entities are among the main reasons for poor data quality, so finding and configuring the right deduplication solution is essential.
Existing data matching benchmarks focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not support the exploration of data matching results.
To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching solutions, we present the Frost platform.
It combines existing benchmarks, established quality metrics, cost and effort metrics, and exploration techniques, making it the first platform to allow systematic exploration to understand matching results.
Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results, as shown in Figure 1.
A deep observation campaign carried out by the High Energy Stereoscopic System (HESS) on Centaurus A enabled the discovery of gamma-rays from the blazar 1ES 1312-423, 2 degrees away from the radio galaxy. With a differential flux at 1 TeV of phi(1 TeV) = (1.9 +/- 0.6(stat) +/- 0.4(sys)) x 10(-13) cm(-2) s(-1) TeV-1 corresponding to 0.5 per cent of the Crab nebula differential flux and a spectral index Gamma = 2.9 +/- 0.5(stat) +/- 0.2(sys), 1ES 1312-423 is one of the faintest sources ever detected in the very high energy (E > 100 GeV) extragalactic sky. A careful analysis using three and a half years of Fermi Large Area Telescope (Fermi-LAT) data allows the discovery at high energies (E > 100 MeV) of a hard spectrum (Gamma = 1.4 +/- 0.4(stat) +/- 0.2(sys)) source coincident with 1ES 1312-423. Radio, optical, UV and X-ray observations complete the spectral energy distribution of this blazar, now covering 16 decades in energy. The emission is successfully fitted with a synchrotron self-Compton model for the non-thermal component, combined with a blackbody spectrum for the optical emission from the host galaxy.
The quasar PKS 1510-089 (z = 0.361) was observed with the H.E.S.S. array of imaging atmospheric Cherenkov telescopes during high states in the optical and GeV bands, to search for very high energy (VHE, defined as E >= 0.1 TeV) emission. VHE gamma-rays were detected with a statistical significance of 9.2 standard deviations in 15.8 h of H. E. S. S. data taken during March and April 2009. A VHE integral flux of I(0.15 TeV < E < 1.0TeV) = (1.0 +/- 0.2(stat) +/- 0.2(sys)) x 10(-11) cm(-2) s(-1) is measured. The best-fit power law to the VHE data has a photon index of G = 5.4 +/- 0.7(stat) +/- 0.3(sys). The GeV and optical light curves show pronounced variability during the period of H.E.S.S. observations. However, there is insufficient evidence to claim statistically significant variability in the VHE data. Because of its relatively high redshift, the VHE flux from PKS 1510-089 should suffer considerable attenuation in the intergalactic space due to the extragalactic background light (EBL). Hence, the measured gamma-ray spectrum is used to derive upper limits on the opacity due to EBL, which are found to be comparable with the previously derived limits from relatively-nearby BL Lac objects. Unlike typical VHE-detected blazars where the broadband spectrum is dominated by nonthermal radiation at all wavelengths, the quasar PKS 1510-089 has a bright thermal component in the optical to UV frequency band. Among all VHE detected blazars, PKS 1510-089 has the most luminous broad line region. The detection of VHE emission from this quasar indicates a low level of gamma - gamma absorption on the internal optical to UV photon field.
HESS J1640-465 - an exceptionally luminous TeV gamma-ray supernova remnant (vol 439, pg 2828, 2014)
(2014)
The results of follow-up observations of the TeV gamma-ray source HESS J1640-465 from 2004 to 2011 with the High Energy Stereoscopic System (HESS) are reported in this work. The spectrum is well described by an exponential cut-off power law with photon index Gamma = 2.11 +/- 0.09(stat) +/- 0.10(sys), and a cut-off energy of E-2 = 6.0(-1.2)(+2.0) TeV. The TeV emission is significantly extended and overlaps with the northwestern part of the shell of the SNR G338.3-0.0. The new HESS results, a re-analysis of archival XMM-Newton data and multiwavelength observations suggest that a significant part of the gamma-ray emission from HESS J1640-465 originates in the supernova remnant shell. In a hadronic scenario, as suggested by the smooth connection of the GeV and TeV spectra, the product of total proton energy and mean target density could be as high as W(p)n(H) similar to 4 x 10(52)(d/10kpc)(2) erg cm(-3).
Composite supernova remnants (SNRs) constitute a small subclass of the remnants of massive stellar explosions where non-thermal radiation is observed from both the expanding shell-like shock front and from a pulsar wind nebula (PWN) located inside of the SNR. These systems represent a unique evolutionary phase of SNRs where observations in the radio, X-ray, and gamma-ray regimes allow the study of the co-evolution of both these energetic phenomena. In this article, we report results from observations of the shell-type SNR G15.4+0.1 performed with the High Energy Stereoscopic System (H. E. S. S.) and XMM-Newton. A compact TeV gamma-ray source, HESS J1818-154, located in the center and contained within the shell of G15.4+0.1 is detected by H. E. S. S. and featurs a spectrum best represented by a power-law model with a spectral index of -2.3 +/- 0.3(stat) +/- 0.2(sys) and an integral flux of F(>0.42 TeV) = (0.9 +/- 0.3(stat) +/- 0.2(sys)) x 10(-12) cm(-2) s(-1). Furthermore, a recent observation with XMM-Newton reveals extended X-ray emission strongly peaked in the center of G15.4+0.1. The X-ray source shows indications of an energy-dependent morphology featuring a compact core at energies above 4 keV and more extended emission that fills the entire region within the SNR at lower energies. Together, the X-ray and VHE gamma-ray emission provide strong evidence of a PWN located inside the shell of G15.4+0.1 and this SNR can therefore be classified as a composite based on these observations. The radio, X-ray, and gamma-ray emission from the PWN is compatible with a one-zone leptonic model that requires a low average magnetic field inside the emission region. An unambiguous counterpart to the putative pulsar, which is thought to power the PWN, has been detected neither in radio nor in X-ray observations of G15.4+0.1.
HESS observations of the binary system PSR B1259-63/LS 2883 around the 2010/2011 periastron passage
(2013)
Aims. We present very high energy (VHE; E > 100 GeV) data from the gamma-ray binary system PSR B1259-63/LS 2883 taken around its periastron passage on 15th of December 2010 with the High Energy Stereoscopic System (H. E. S. S.) of Cherenkov Telescopes. We aim to search for a possible TeV counterpart of the GeV flare detected by the Fermi LAT. In addition, we aim to study the current periastron passage in the context of previous observations taken at similar orbital phases, testing the repetitive behaviour of the source.
Methods. Observations at VHEs were conducted with H.E.S.S. from 9th to 16th of January 2011. The total dataset amounts to similar to 6 h of observing time. The data taken around the 2004 periastron passage were also re-analysed with the current analysis techniques in order to extend the energy spectrum above 3 TeV to fully compare observation results from 2004 and 2011.
Results. The source is detected in the 2011 data at a significance level of 11.5 sigma revealing an averaged integral flux above 1 TeV of (1.01 +/- 0.18(stat) +/- 0.20(sys)) x 10(-12) cm(-2) s(-1). The differential energy spectrum follows a power-law shape with a spectral index Gamma = 2.92 +/- 0.30(stat) +/- 0.20(sys) and a flux normalisation at 1 TeV of N-0 = (1.95 +/- 0.32(stat) +/- 0.39(sys)) x 10(-12) TeV-1 cm(-2) s(-1). The measured light curve does not show any evidence for variability of the source on the daily scale. The re-analysis of the 2004 data yields results compatible with the published ones. The differential energy spectrum measured up to similar to 10 TeV is consistent with a power law with a spectral index Gamma = 2.81 +/- 0.10(stat) +/- 0.20(sys) and a flux normalisation at 1 TeV of N-0 = (1.29 +/- 0.08(stat) +/- 0.26(sys)) x 10(-12) TeV-1 cm(-2) s(-1).
Conclusions. The measured integral flux and the spectral shape of the 2011 data are compatible with the results obtained around previous periastron passages. The absence of variability in the H.E.S.S. data indicates that the GeV flare observed by Fermi LAT in the time period covered also by H.E.S.S. observations originates in a different physical scenario than the TeV emission. Moreover, the comparison of the new results to the results from the 2004 observations made at a similar orbital phase provides a stronger evidence of the repetitive behaviour of the source.
Context. On March 4, 2013 the Fermi-EAT and AGILE reported a flare from the direction of the Crab nebula in which the high-energy (HE; E > 100 MeV) flux was six times above its quiescent level. Simultaneous observations in other energy bands give us hints about the emission processes during the flare episode and the physics of pulsar wind nebulae in general.
Aims. We search for variability in the emission of the Crab nebula at very-high energies (VHF,; E > 100 GeV), using contemporaneous data taken with the H.E.S.S. array of Cherenkov telescopes.
Methods. Observational data taken with the H.E.S.S. instrument on five consecutive days during the flare were analysed for the flux and spectral shape of the emission from the Crab nebula. Night-wise light curves are presented with energy thresholds of 1 TeV and 5 TeV.
Results. The observations conducted with H.E.S.S. on March 6 to March 10, 2013 show no significant changes in the flux. They limit the variation in the integral flux above 1 TeV to less than 63% and the integral flux above 5 TeV to less than 78% at a 95% confidence level.
Unique column combinations (UCCs) are a fundamental concept in relational databases. They identify entities in the data and support various data management activities. Still, UCCs are usually not explicitly defined and need to be discovered. State-of-the-art data profiling algorithms are able to efficiently discover UCCs in moderately sized datasets, but they tend to fail on large and, in particular, on wide datasets due to run time and memory limitations. <br /> In this paper, we introduce HPIValid, a novel UCC discovery algorithm that implements a faster and more resource-saving search strategy. HPIValid models the metadata discovery as a hitting set enumeration problem in hypergraphs. In this way, it combines efficient discovery techniques from data profiling research with the most recent theoretical insights into enumeration algorithms. Our evaluation shows that HPIValid is not only orders of magnitude faster than related work, it also has a much smaller memory footprint.
Primary keys (PKs) and foreign keys (FKs) are important elements of relational schemata in various applications, such as query optimization and data integration. However, in many cases, these constraints are unknown or not documented. Detecting them manually is time-consuming and even infeasible in large-scale datasets. We study the problem of discovering primary keys and foreign keys automatically and propose an algorithm to detect both, namely Holistic Primary Key and Foreign Key Detection (HoPF). PKs and FKs are subsets of the sets of unique column combinations (UCCs) and inclusion dependencies (INDs), respectively, for which efficient discovery algorithms are known. Using score functions, our approach is able to effectively extract the true PKs and FKs from the vast sets of valid UCCs and INDs. Several pruning rules are employed to speed up the procedure. We evaluate precision and recall on three benchmarks and two real-world datasets. The results show that our method is able to retrieve on average 88% of all primary keys, and 91% of all foreign keys. We compare the performance of HoPF with two baseline approaches that both assume the existence of primary keys.
How inclusive are we?
(2022)
ACM SIGMOD, VLDB and other database organizations have committed to fostering an inclusive and diverse community, as do many other scientific organizations. Recently, different measures have been taken to advance these goals, especially for underrepresented groups. One possible measure is double-blind reviewing, which aims to hide gender, ethnicity, and other properties of the authors. <br /> We report the preliminary results of a gender diversity analysis of publications of the database community across several peer-reviewed venues, and also compare women's authorship percentages in both single-blind and double-blind venues along the years. We also obtained a cross comparison of the obtained results in data management with other relevant areas in Computer Science.
Introducing the CTA concept
(2013)
The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project.
The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity-duplicates-into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise. <br /> We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent.
MDedup
(2020)
Duplicate detection is an integral part of data cleaning and serves to identify multiple representations of same real-world entities in (relational) datasets. Existing duplicate detection approaches are effective, but they are also hard to parameterize or require a lot of pre-labeled training data. Both parameterization and pre-labeling are at least domain-specific if not dataset-specific, which is a problem if a new dataset needs to be cleaned.
For this reason, we propose a novel, rule-based and fully automatic duplicate detection approach that is based on matching dependencies (MDs). Our system uses automatically discovered MDs, various dataset features, and known gold standards to train a model that selects MDs as duplicate detection rules. Once trained, the model can select useful MDs for duplicate detection on any new dataset. To increase the generally low recall of MD-based data cleaning approaches, we propose an additional boosting step. Our experiments show that this approach reaches up to 94% F-measure and 100% precision on our evaluation datasets, which are good numbers considering that the system does not require domain or target data-specific configuration.
Data analytics are moving beyond the limits of a single data processing platform. A cross-platform query optimizer is necessary to enable applications to run their tasks over multiple platforms efficiently and in a platform-agnostic manner. For the optimizer to be effective, it must consider data movement costs across different data processing platforms. In this paper, we present the graph-based data movement strategy used by RHEEM, our open-source cross-platform system. In particular, we (i) model the data movement problem as a new graph problem, which we prove to be NP-hard, and (ii) propose a novel graph exploration algorithm, which allows RHEEM to discover multiple hidden opportunities for cross-platform data processing.
Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is CSV. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard CSV formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic lpollutionz process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.
Aims. Previous observations with the High Energy Stereoscopic System (H.E.S.S.) have revealed an extended very-high-energy (VHE; E > 100 GeV) gamma-ray source, HESS J1834-087, coincident with the supernova remnant (SNR) W41. The origin of the gamma-ray emission was investigated in more detail with the H.E.S.S. array and the Large Area Telescope (LAT) onboard the Fermi Gamma-ray Space Telescope.
Methods. The gamma-ray data provided by 61 h of observations with H.E.S.S., and four years with the Fermi LAT were analyzed, covering over five decades in energy from 1.8 GeV up to 30 TeV. The morphology and spectrum of the TeV and GeV sources were studied and multiwavelength data were used to investigate the origin of the gamma-ray emission toward W41.
Results. The TeV source can be modeled with a sum of two components: one point-like and one significantly extended (sigma(TeV) = 0.17 degrees +/- 0.01 degrees), both centered on SNR W41 and exhibiting spectra described by a power law with index Gamma(TeV) similar or equal to 2.6. The GeV source detected with Fermi LAT is extended (sigma(GeV) = 0.15 degrees +/- 0.03 degrees) and morphologically matches the VHE emission. Its spectrum can be described by a power-law model with an index Gamma(GeV) = 2.15 +/- 0.12 and smoothly joins the spectrum of the whole TeV source. A break appears in the gamma-ray spectra around 100 GeV. No pulsations were found in the GeV range.
Conclusions. Two main scenarios are proposed to explain the observed emission: a pulsar wind nebula (PWN) or the interaction of SNR W41 with an associated molecular cloud. X-ray observations suggest the presence of a point-like source (a pulsar candidate) near the center of the remnant and nonthermal X-ray diffuse emission that could arise from the possibly associated PWN. The PWN scenario is supported by the compatible positions of the TeV and GeV sources with the putative pulsar. However, the spectral energy distribution from radio to gamma-rays is reproduced by a one-zone leptonic model only if an excess of low-energy electrons is injected following a Maxwellian distribution by a pulsar with a high spin-down power (> 10(37) erg s(-1)). This additional low-energy component is not needed if we consider that the point-like TeV source is unrelated to the extended GeV and TeV sources. The interacting SNR scenario is supported by the spatial coincidence between the gamma-ray sources, the detection of OH (1720 MHz) maser lines, and the hadronic modeling.
Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application. Commonly used technologies, such as J2EE and .NET, form de facto standards for the realization of complex distributed systems. Evolution of component systems has lead to web services and service-based architectures. This has been manifested in a multitude of industry standards and initiatives such as XML, WSDL UDDI, SOAP, etc. All these achievements lead to a new and promising paradigm in IT systems engineering which proposes to design complex software solutions as collaboration of contractually defined software services. Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns. The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the Research Scholl, this technical report covers a wide range of research topics. These include but are not limited to: Self-Adaptive Service-Oriented Systems, Operating System Support for Service-Oriented Systems, Architecture and Modeling of Service-Oriented Systems, Adaptive Process Management, Services Composition and Workflow Planning, Security Engineering of Service-Based IT Systems, Quantitative Analysis and Optimization of Service-Oriented Systems, Service-Oriented Systems in 3D Computer Graphics sowie Service-Oriented Geoinformatics.
Proceedings of the HPI Research School on Service-oriented Systems Engineering 2020 Fall Retreat
(2021)
Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application.
Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns.
The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the research school, this technical report covers a wide range of topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment.
RHEEMix in the data jungle
(2020)
Data analytics are moving beyond the limits of a single platform. In this paper, we present the cost-based optimizer of Rheem, an open-source cross-platform system that copes with these new requirements. The optimizer allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to determine efficient data movement plans among subtasks and platforms; and (iii) an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. We show that our optimizer can perform tasks more than one order of magnitude faster when using multiple platforms than when using a single platform.
RHEEMix in the data jungle
(2020)
Data analytics are moving beyond the limits of a single platform. In this paper, we present the cost-based optimizer of Rheem, an open-source cross-platform system that copes with these new requirements. The optimizer allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to determine efficient data movement plans among subtasks and platforms; and (iii) an efficient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. We show that our optimizer can perform tasks more than one order of magnitude faster when using multiple platforms than when using a single platform.
Context. Very-high-energy (VHE; E > 100 GeV) gamma-ray emission from blazars inevitably gives rise to electron-positron pair production through the interaction of these gamma-rays with the extragalactic background light (EBL). Depending on the magnetic fields in the proximity of the source, the cascade initiated from pair production can result in either an isotropic halo around an initially- beamed source or a magnetically- broadened cascade :aux.
Aims. Both extended pair-halo (PH) and magnetically broadened cascade (MBC) emission from regions surrounding the blazars 1ES 1101-232, IRS 0229+200, and PKS 2155-304 were searched for using VHE y-ray data taken with the High Energy Stereoscopic System (HESS.) and high-energy (HE; 100 MeV < E < 100 GeV) gamma-ray data with the Fermi Large Area Telescope (LAT).
Methods. By comparing the angular distributions of the reconstructed gamma-ray events to the angular profiles calculated from detailed theoretical models, the presence of PH and MBC was investigated.
Results. Upper limits on the extended emission around lES 1101-232, lES 0229+200, and PKS 2155-304 are found to be at a level of a few per cent of the Crab nebula flux above 1 TeV, depending on the assumed photon index of the cascade emission. Assuming strong extra-Galactic magnetic field (EGME) values, >10(-12) G, this limits the production of pair haloes developing from electromagnetic cascades. For weaker magnetic fields, in which electromagnetic cascades would result in MBCs. EGMF strengths in the range (0.3-3) x 10(-15) G were excluded for PKS 2155-304 at the 99% confidence level, under the assumption of a 1 Mpc coherence length.
Gamma-ray line signatures can be expected in the very-high-energy (E-gamma > 100 GeV) domain due to self-annihilation or decay of dark matter (DM) particles in space. Such a signal would be readily distinguishable from astrophysical gamma-ray sources that in most cases produce continuous spectra that span over several orders of magnitude in energy. Using data collected with the H. E. S. S. gamma-ray instrument, upper limits on linelike emission are obtained in the energy range between similar to 500 GeV and similar to 25 TeV for the central part of the Milky Way halo and for extragalactic observations, complementing recent limits obtained with the Fermi-LAT instrument at lower energies. No statistically significant signal could be found. For monochromatic gamma-ray line emission, flux limits of (2 x 10(-7)-2 x 10(-5)) m(-2)s(-1)sr(-1) and (1 x 10(-8)- 2 x 10(-6)) m(-2)s(-1)sr(-1) are obtained for the central part of the Milky Way halo and extragalactic observations, respectively. For a DM particle mass of 1 TeV, limits on the velocity- averaged DM annihilation cross section <sigma upsilon >(chi chi ->gamma gamma) reach similar to 10(-27)cm(3)s(-1), based on the Einasto parametrization of the Galactic DM halo density profile. DOI: 10.1103/PhysRevLett.110.041301
Search for TeV Gamma-ray emission from GRB 100621A, an extremely bright GRB in X-rays, with HESS
(2014)
The long gamma-ray burst (GRB) 100621A, at the time the brightest X-ray transient ever detected by Swift-XRT in the 0.3-10 keV range, has been observed with the H.E.S.S. imaging air Cherenkov telescope array, sensitive to gamma radiation in the very-high-energy (VHE, >100 GeV) regime. Due to its relatively small redshift of z similar to 0.5, the favourable position in the southern sky and the relatively short follow-up time (<700 s after the satellite trigger) of the H.E.S.S. observations, this GRB could be within the sensitivity reach of the HESS. instrument. The analysis of the HESS. data shows no indication of emission and yields an integral flux upper limit above similar to 380 GeV of 4.2 x 10(-12) cm(-2) s(-1) s (95% confidence level), assuming a simple Band function extension model. A comparison to a spectral-temporal model, normalised to the prompt flux at sub-MeV energies, constraints the existence of a temporally extended and strong additional hard power law, as has been observed in the other bright X-ray GRB 130427A. A comparison between the HESS. upper limit and the contemporaneous energy output in X-rays constrains the ratio between the X-ray and VHE gamma-ray fluxes to be greater than 0.4. This value is an important quantity for modelling the afterglow and can constrain leptonic emission scenarios, where leptons are responsible for the X-ray emission and might produce VHE gamma rays.
Context. Globular clusters (GCs) are established emitters of high-energy (HE, 100 MeV < E < 100 GeV) gamma-ray radiation which could originate from the cumulative emission of the numerous millisecond pulsars (msPSRs) in the clusters’ cores or from inverse Compton (IC) scattering of relativistic leptons accelerated in the GC environment. These stellar clusters could also constitute a new class of sources in the very-high-energy (VHE, E > 100 GeV) gamma-ray regime, judging from the recent detection of a signal from the direction of Terzan 5 with the H.E.S.S. telescope array. Aims. To search for VHE gamma-ray sources associated with other GCs, and to put constraints on leptonic emission models, we systematically analyzed the observations towards 15 GCs taken with the H. E. S. S. array of imaging atmospheric Cherenkov telescopes. Methods. We searched for point-like and extended VHE gamma-ray emission from each GC in our sample and also performed a stacking analysis combining the data from all GCs to investigate the hypothesis of a population of faint emitters. Assuming IC emission as the origin of the VHE gamma-ray signal from the direction of Terzan 5, we calculated the expected gamma-ray flux from each of the 15 GCs, based on their number of millisecond pulsars, their optical brightness and the energy density of background photon fields. Results. We did not detect significant VHE gamma-ray emission from any of the 15 GCs in either of the two analyses. Given the uncertainties related to the parameter determinations, the obtained flux upper limits allow to rule out the simple IC/msPSR scaling model for NGC6388 and NGC7078. The upper limits derived from the stacking analyses are factors between 2 and 50 below the flux predicted by the simple leptonic scaling model, depending on the assumed source extent and the dominant target photon fields. Therefore, Terzan 5 still remains exceptional among all GCs, as the VHE gamma-ray emission either arises from extra-ordinarily efficient leptonic processes, or from a recent catastrophic event, or is even unrelated to the GC itself.
Duplicate detection consists in determining different representations of real-world objects in a database. Recent research has considered the use of relationships among object representations to improve duplicate detection. In the general case where relationships form a graph, research has mainly focused on duplicate detection quality/effectiveness. Scalability has been neglected so far, even though it is crucial for large real-world duplicate detection tasks. In this paper we scale up duplicate detection in graph data (DDG) to large amounts of data and pairwise comparisons, using the support of a relational database system. To this end, we first generalize the process of DDG. We then present how to scale algorithms for DDG in space (amount of data processed with limited main memory) and in time. Finally, we explore how complex similarity computation can be performed efficiently. Experiments on data an order of magnitude larger than data considered so far in DDG clearly show that our methods scale to large amounts of data not residing in main memory.
TeV gamma-ray observations of the young synchrotron-dominated SNRs G1.9+0.3 and G330.2+1.0 with HESS
(2014)
The non-thermal nature of the X-ray emission from the shell-type supernova remnants (SNRs) G1.9+0.3 and G330.2+1.0 is an indication of intense particle acceleration in the shock fronts of both objects. This suggests that the SNRs are prime candidates for very-high-energy (VHE; E > 0.1 TeV) gamma-ray observations. G1.9+0.3, recently established as the youngest known SNR in the Galaxy, also offers a unique opportunity to study the earliest stages of SNR evolution in the VHE domain. The purpose of this work is to probe the level of VHE gamma-ray emission from both SNRs and use this to constrain their physical properties. Observations were conducted with the H. E. S. S. (High Energy Stereoscopic System) Cherenkov Telescope Array over a more than six-year period spanning 2004-2010. The obtained data have effective livetimes of 67 h for G1.9+0.3 and 16 h for G330.2+1.0. The data are analysed in the context of the multiwavelength observations currently available and in the framework of both leptonic and hadronic particle acceleration scenarios. No significant gamma-ray signal from G1.9+0.3 or G330.2+1.0 was detected. Upper limits (99 per cent confidence level) to the TeV flux from G1.9+0.3 and G330.2+1.0 for the assumed spectral index Gamma = 2.5 were set at 5.6 x 10(-1)3 cm(-2) s(-1) above 0.26 TeV and 3.2 x 10(-12) cm(-2) s(-1) above 0.38 TeV, respectively. In a one-zone leptonic scenario, these upper limits imply lower limits on the interior magnetic field to B-G1.9 greater than or similar to 12 mu G for G1.9+0.3 and to B-G330 greater than or similar to 8 mu G for G330.2+1.0. In a hadronic scenario, the low ambient densities and the large distances to the SNRs result in very low predicted fluxes, for which the H.E.S.S. upper limits are not constraining.
The 2010 very high energy gamma-ray flare and 10 years ofmulti-wavelength oservations of M 87
(2012)
The giant radio galaxy M 87 with its proximity (16 Mpc), famous jet, and very massive black hole ((3-6) x 10(9) M-circle dot) provides a unique opportunity to investigate the origin of very high energy (VHE; E > 100 GeV) gamma-ray emission generated in relativistic outflows and the surroundings of supermassive black holes. M 87 has been established as a VHE gamma-ray emitter since 2006. The VHE gamma-ray emission displays strong variability on timescales as short as a day. In this paper, results from a joint VHE monitoring campaign on M 87 by the MAGIC and VERITAS instruments in 2010 are reported. During the campaign, a flare at VHE was detected triggering further observations at VHE (H.E.S.S.), X-rays (Chandra), and radio (43 GHz Very Long Baseline Array, VLBA). The excellent sampling of the VHE gamma-ray light curve enables one to derive a precise temporal characterization of the flare: the single, isolated flare is well described by a two-sided exponential function with significantly different flux rise and decay times of tau(rise)(d) = (1.69 +/- 0.30) days and tau(decay)(d) = (0.611 +/- 0.080) days, respectively. While the overall variability pattern of the 2010 flare appears somewhat different from that of previous VHE flares in 2005 and 2008, they share very similar timescales (similar to day), peak fluxes (Phi(>0.35 TeV) similar or equal to (1-3) x 10(-11) photons cm(-2) s(-1)), and VHE spectra. VLBA radio observations of 43 GHz of the inner jet regions indicate no enhanced flux in 2010 in contrast to observations in 2008, where an increase of the radio flux of the innermost core regions coincided with a VHE flare. On the other hand, Chandra X-ray observations taken similar to 3 days after the peak of the VHE gamma-ray emission reveal an enhanced flux from the core (flux increased by factor similar to 2; variability timescale <2 days). The long-term (2001-2010) multi-wavelength (MWL) light curve of M 87, spanning from radio to VHE and including data from Hubble Space Telescope, Liverpool Telescope, Very Large Array, and European VLBI Network, is used to further investigate the origin of the VHE gamma-ray emission. No unique, common MWL signature of the three VHE flares has been identified. In the outer kiloparsec jet region, in particular in HST-1, no enhanced MWL activity was detected in 2008 and 2010, disfavoring it as the origin of the VHE flares during these years. Shortly after two of the three flares (2008 and 2010), the X-ray core was observed to be at a higher flux level than its characteristic range (determined from more than 60 monitoring observations: 2002-2009). In 2005, the strong flux dominance of HST-1 could have suppressed the detection of such a feature. Published models for VHE gamma-ray emission from M 87 are reviewed in the light of the new data.
The gamma-ray spectrum of the low-frequency-peaked BL Lac (LBL) object AP Librae is studied, following the discovery of very-high-energy (VHE; E > 100 GeV) gamma-ray emission up to the TeV range by the H.E.S.S. experiment. Thismakes AP Librae one of the few VHE emitters of the LBL type. The measured spectrum yields a flux of (8.8 +/- 1.5(stat) +/- 1.8(sys)) x 10(-12) cm(-2) s(-1) above 130 GeV and a spectral index of Gamma = 2.65 +/- 0.19(stat) +/- 0.20(sys). This study also makes use of Fermi-LAT observations in the high energy (HE, E > 100 MeV) range, providing the longest continuous light curve (5 years) ever published on this source. The source underwent a flaring event between MJD 56 306-56 376 in the HE range, with a flux increase of a factor of 3.5 in the 14 day bin light curve and no significant variation in spectral shape with respect to the low-flux state. While the H.E.S.S. and (low state) Fermi-LAT fluxes are in good agreement where they overlap, a spectral curvature between the steep VHE spectrum and the Fermi-LAT spectrum is observed. The maximum of the gamma-ray emission in the spectral energy distribution is located below the GeV energy range.
The task of expert finding is to rank the experts in the search space given a field of expertise as an input query. In this paper, we propose a topic modeling approach for this task. The proposed model uses latent Dirichlet allocation (LDA) to induce probabilistic topics. In the first step of our algorithm, the main topics of a document collection are extracted using LDA. The extracted topics present the connection between expert candidates and user queries. In the second step, the topics are used as a bridge to find the probability of selecting each candidate for a given query. The candidates are then ranked based on these probabilities. The experimental results on the Text REtrieval Conference (TREC) Enterprise track for 2005 and 2006 show that the proposed topic-based approach outperforms the state-of-the-art profile- and document-based models, which use information retrieval methods to rank experts. Moreover, we present the superiority of the proposed topic-based approach to the improved document-based expert finding systems, which consider additional information such as local context, candidate prior, and query expansion.
Duplicate detection algorithms produce clusters of database records, each cluster representing a single real-world entity. As most of these algorithms use pairwise comparisons, the resulting (transitive) clusters can be inconsistent: Not all records within a cluster are sufficiently similar to be classified as duplicate. Thus, one of many subsequent clustering algorithms can further improve the result. <br /> We explain in detail, compare, and evaluate many of these algorithms and introduce three new clustering algorithms in the specific context of duplicate detection. Two of our three new algorithms use the structure of the input graph to create consistent clusters. Our third algorithm, and many other clustering algorithms, focus on the edge weights, instead. For evaluation, in contrast to related work, we experiment on true real-world datasets, and in addition examine in great detail various pair-selection strategies used in practice. While no overall winner emerges, we are able to identify best approaches for different situations. In scenarios with larger clusters, our proposed algorithm, Extended Maximum Clique Clustering (EMCC), and Markov Clustering show the best results. EMCC especially outperforms Markov Clustering regarding the precision of the results and additionally has the advantage that it can also be used in scenarios where edge weights are not available.
Extract-Transform-Load (ETL) tools are used for the creation, maintenance, and evolution of data warehouses, data marts, and operational data stores. ETL workflows populate those systems with data from various data sources by specifying and executing a DAG of transformations. Over time, hundreds of individual workflows evolve as new sources and new requirements are integrated into the system. The maintenance and evolution of large-scale ETL systems requires much time and manual effort. A key problem is to understand the meaning of unfamiliar attribute labels in source and target databases and ETL transformations. Hard-to-understand attribute labels lead to frustration and time spent to develop and understand ETL workflows. We present a schema decryption technique to support ETL developers in understanding cryptic schemata of sources, targets, and ETL transformations. For a given ETL system, our recommender-like approach leverages the large number of mapped attribute labels in existing ETL workflows to produce good and meaningful decryptions. In this way we are able to decrypt attribute labels consisting of a number of unfamiliar few-letter abbreviations, such as UNP_PEN_INT, which we can decrypt to UNPAID_PENALTY_INTEREST. We evaluate our schema decryption approach on three real-world repositories of ETL workflows and show that our approach is able to suggest high-quality decryptions for cryptic attribute labels in a given schema.
VLDB 2021
(2021)
The 47th International Conference on Very Large Databases (VLDB'21) was held on August 16-20, 2021 as a hybrid conference. It attracted 180 in-person attendees in Copenhagen and 840 remote attendees. In this paper, we describe our key decisions as general chairs and program committee chairs and share the lessons we learned.