TY  - BOOK
A1  - Draisbach, Uwe
A1  - Naumann, Felix
A1  - Szott, Sascha
A1  - Wonneberg, Oliver
T1  - Adaptive windows for duplicate detection
N2  - 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).
N2  - Duplikaterkennung beschreibt das Auffinden von mehreren Datensätzen, die das gleiche Realwelt-Objekt repräsentieren. Diese Aufgabe ist nicht trivial, da sich (i) die Datensätze geringfügig unterscheiden können, so dass Ähnlichkeitsmaße für einen paarweisen Vergleich benötigt werden, und (ii) aufgrund der Datenmenge ein vollständiger, paarweiser Vergleich nicht möglich ist. Zur Lösung des zweiten Problems existieren verschiedene Algorithmen, die die Datenmenge partitionieren und nur noch innerhalb der Partitionen Vergleiche durchführen. Einer dieser Algorithmen ist die Sorted-Neighborhood-Methode (SNM), welche Daten anhand eines Schlüssels sortiert und dann ein Fenster über die sortierten Daten schiebt. Vergleiche werden nur innerhalb dieses Fensters durchgeführt. Wir beschreiben verschiedene Variationen der Sorted-Neighborhood-Methode, die auf variierenden Fenstergrößen basieren. Diese Ansätze basieren auf der Intuition, dass Bereiche mit größerer und geringerer Ähnlichkeiten innerhalb der sortierten Datensätze existieren, für die entsprechend größere bzw. kleinere Fenstergrößen sinnvoll sind. Wir beschreiben und evaluieren verschiedene Adaptierungs-Strategien, von denen nachweislich einige bezüglich Effizienz besser sind als die originale Sorted-Neighborhood-Methode (gleiches Ergebnis bei weniger Vergleichen).
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 49 
KW  - Informationssysteme
KW  - Datenqualität
KW  - Datenintegration
KW  - Duplikaterkennung
KW  - Duplicate Detection
KW  - Data Quality
KW  - Data Integration
KW  - Information Systems
Y1  - 2012
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-53007
SN  - 978-3-86956-143-1
SN  - 1613-5652
SN  - 2191-1665
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  - 
TY  - BOOK
A1  - Abedjan, Ziawasch
A1  - Naumann, Felix
T1  - Advancing the discovery of unique column combinations
N2  - 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.
N2  - Unique-Spaltenkombinationen sind Spaltenkombinationen einer Datenbanktabelle, die nur einzigartige Werte beinhalten. Das Finden von Unique-Spaltenkombinationen spielt sowohl eine wichtige Rolle im Bereich der Grundlagenforschung von Informationssystemen als auch in Anwendungsgebieten wie dem Datenmanagement und der Erkenntnisgewinnung aus Datenbeständen. Vorhandene Algorithmen, die dieses Problem angehen, sind entweder Brute-Force oder benötigen zu viel Hauptspeicher. Deshalb können diese Algorithmen nur auf kleine Datenmengen angewendet werden. In dieser Arbeit werden der bekannte GORDIAN-Algorithmus und Apriori-basierte Algorithmen zum Zwecke weiterer Optimierung analysiert. Wir verbessern die Apriori Algorithmen durch eine effiziente Kandidatengenerierung und Heuristikbasierten Kandidatenfilter. Eine Hybride Lösung, HCA-GORDIAN, kombiniert die Vorteile von GORDIAN und unserem neuen Algorithmus HCA, welche die bisherigen Algorithmen hinsichtlich der Effizienz in vielen Situationen übertrifft.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 51 
KW  - Apriori
KW  - eindeutig
KW  - funktionale Abhängigkeit
KW  - Schlüsselentdeckung
KW  - Data Profiling
KW  - apriori
KW  - unique
KW  - functional dependency
KW  - key discovery
KW  - data profiling
Y1  - 2011
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-53564
SN  - 978-3-86956-148-6
SN  - 1613-5652
SN  - 2191-1665
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  - 
TY  - JOUR
A1  - Abramowski, Attila
A1  - Acero, F.
A1  - Aharonian, Felix A.
A1  - Benkhali, Faical  Ait
A1  - Akhperjanian, A. G.
A1  - Angüner, Ekrem Oǧuzhan
A1  - Anton, Gisela
A1  - Balenderan, Shangkari
A1  - Balzer, Arnim
A1  - Barnacka, Anna
A1  - Becherini, Yvonne
A1  - Tjus, J. Becker
A1  - Bernlöhr, K.
A1  - Birsin, E.
A1  - Bissaldi, E.
A1  - Biteau, Jonathan
A1  - Boisson, Catherine
A1  - Bolmont, J.
A1  - Bordas, Pol
A1  - Brucker, J.
A1  - Brun, Francois
A1  - Brun, Pierre
A1  - Bulik, Tomasz
A1  - Carrigan, Svenja
A1  - Casanova, Sabrina
A1  - Cerruti, M.
A1  - Chadwick, Paula M.
A1  - Chalme-Calvet, R.
A1  - Chaves, Ryan C. G.
A1  - Cheesebrough, A.
A1  - Chretien, M.
A1  - Colafrancesco, Sergio
A1  - Cologna, Gabriele
A1  - Conrad, Jan
A1  - Couturier, C.
A1  - Dalton, M.
A1  - Daniel, M. K.
A1  - Davids, I. D.
A1  - Degrange, B.
A1  - Deil, C.
A1  - deWilt, P.
A1  - Dickinson, H. J.
A1  - Djannati-Ataï, A.
A1  - Domainko, W.
A1  - Drury, L. O'C.
A1  - Dubus, G.
A1  - Dutson, K.
A1  - Dyks, J.
A1  - Dyrda, M.
A1  - Edwards, T.
A1  - Egberts, Kathrin
A1  - Eger, P.
A1  - Espigat, P.
A1  - Farnier, C.
A1  - Fegan, S.
A1  - Feinstein, F.
A1  - Fernandes, M. V.
A1  - Fernandez, D.
A1  - Fiasson, A.
A1  - Fontaine, G.
A1  - Foerster, A.
A1  - Fuessling, M.
A1  - Gajdus, M.
A1  - Gallant, Y. A.
A1  - Garrigoux, T.
A1  - Gast, H.
A1  - Giebels, B.
A1  - Glicenstein, J. F.
A1  - Goering, D.
A1  - Grondin, M. -H.
A1  - Grudzinska, M.
A1  - Haeffner, S.
A1  - Hague, J. D.
A1  - Hahn, J.
A1  - Harris, J.
A1  - Heinzelmann, G.
A1  - Henri, G.
A1  - Hermann, G.
A1  - Hervet, O.
A1  - Hillert, A.
A1  - Hinton, James Anthony
A1  - Hofmann, W.
A1  - Hofverberg, P.
A1  - Holler, Markus
A1  - Horns, D.
A1  - Jacholkowska, A.
A1  - Jahn, C.
A1  - Jamrozy, M.
A1  - Janiak, M.
A1  - Jankowsky, F.
A1  - Jung, I.
A1  - Kastendieck, M. A.
A1  - Katarzynski, K.
A1  - Katz, U.
A1  - Kaufmann, S.
A1  - Khelifi, B.
A1  - Kieffer, M.
A1  - Klepser, S.
A1  - Klochkov, D.
A1  - Kluzniak, W.
A1  - Kneiske, T.
A1  - Kolitzus, D.
A1  - Komin, Nu.
A1  - Kosack, K.
A1  - Krakau, S.
A1  - Krayzel, F.
A1  - Krueger, P. P.
A1  - Laffon, H.
A1  - Lamanna, G.
A1  - Lefaucheur, J.
A1  - Lemoine-Goumard, M.
A1  - Lenain, J-P.
A1  - Lennarz, D.
A1  - Lohse, T.
A1  - Lopatin, A.
A1  - Lu, C-C.
A1  - Marandon, V.
A1  - Marcowith, Alexandre
A1  - Marx, R.
A1  - Maurin, G.
A1  - Maxted, N.
A1  - Mayer, M.
A1  - McComb, T. J. L.
A1  - Medina, M. C.
A1  - Mehault, J.
A1  - Menzler, U.
A1  - Meyer, M.
A1  - Moderski, R.
A1  - Mohamed, M.
A1  - Moulin, Emmanuel
A1  - Murach, T.
A1  - Naumann, C. L.
A1  - de Naurois, M.
A1  - Nedbal, D.
A1  - Niemiec, J.
A1  - Nolan, S. J.
A1  - Oakes, L.
A1  - Ohm, S.
A1  - Wilhelmi, E. de Ona
A1  - Opitz, B.
A1  - Ostrowski, M.
A1  - Oya, I.
A1  - Panter, M.
A1  - Parsons, R. D.
A1  - Arribas, M. Paz
A1  - Pekeur, N. W.
A1  - Pelletier, G.
A1  - Perez, J.
A1  - Petrucci, P-O.
A1  - Peyaud, B.
A1  - Pita, S.
A1  - Poon, H.
A1  - Puehlhofer, G.
A1  - Punch, M.
A1  - Quirrenbach, A.
A1  - Raab, S.
A1  - Raue, M.
A1  - Reimer, A.
A1  - Reimer, O.
A1  - Renaud, M.
A1  - de los Reyes, R.
A1  - Rieger, F.
A1  - Rob, L.
A1  - Rosier-Lees, S.
A1  - Rowell, G.
A1  - Rudak, B.
A1  - Rulten, C. B.
A1  - Sahakian, V.
A1  - Sanchez, David M.
A1  - Santangelo, A.
A1  - Schlickeiser, R.
A1  - Schuessler, F.
A1  - Schulz, A.
A1  - Schwanke, U.
A1  - Schwarzburg, S.
A1  - Schwemmer, S.
A1  - Sol, H.
A1  - Spengler, G.
A1  - Spiess, F.
A1  - Stawarz, L.
A1  - Steenkamp, R.
A1  - Stegmann, Christian
A1  - Stinzing, F.
A1  - Stycz, K.
A1  - Sushch, Iurii
A1  - Szostek, A.
A1  - Tavernet, J-P.
A1  - Terrier, R.
A1  - Tluczykont, M.
A1  - Trichard, C.
A1  - Valerius, K.
A1  - van Eldik, C.
A1  - Vasileiadis, G.
A1  - Venter, C.
A1  - Viana, A.
A1  - Vincent, P.
A1  - Voelk, H. J.
A1  - Volpe, F.
A1  - Vorster, M.
A1  - Wagner, S. J.
A1  - Wagner, P.
A1  - Ward, M.
A1  - Weidinger, M.
A1  - Weitzel, Q.
A1  - White, R.
A1  - Wierzcholska, A.
A1  - Willmann, P.
A1  - Woernlein, A.
A1  - Wouters, D.
A1  - Zacharias, M.
A1  - Zajczyk, A.
A1  - Zdziarski, A. A.
A1  - Zech, Alraune
A1  - Zechlin, H-S.
T1  - Constraints on axionlike particles with HESS from the irregularity of the PKS 2155-304 energy spectrum
JF  - Physical review : D, Particles, fields, gravitation, and cosmology
N2  - 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.
Y1  - 2013
U6  - https://doi.org/10.1103/PhysRevD.88.102003
SN  - 1550-7998
SN  - 1550-2368
VL  - 88
IS  - 10
PB  - American Physical Society
CY  - College Park
ER  - 
TY  - BOOK
A1  - Bauckmann, Jana
A1  - Abedjan, Ziawasch
A1  - Leser, Ulf
A1  - Müller, Heiko
A1  - Naumann, Felix
T1  - Covering or complete? : Discovering conditional inclusion dependencies
N2  - 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.
N2  - Datenabhängigkeiten (wie zum Beispiel Integritätsbedingungen), werden verwendet, um die Qualität eines Datenbankschemas zu erhöhen, um Anfragen zu optimieren und um Konsistenz in einer Datenbank sicherzustellen. In den letzten Jahren wurden bedingte Abhängigkeiten (conditional dependencies) vorgestellt, die die Qualität von Daten analysieren und verbessern sollen. Eine bedingte Abhängigkeit ist eine Abhängigkeit mit begrenztem Gültigkeitsbereich, der über Bedingungen auf einem oder mehreren Attributen definiert wird. In diesem Bericht betrachten wir bedingte Inklusionsabhängigkeiten (conditional inclusion dependencies; CINDs). Wir generalisieren die Definition von CINDs anhand der Unterscheidung von überdeckenden (covering) und vollständigen (completeness) Bedingungen. Wir stellen einen Anwendungsfall für solche CINDs vor, der den Nutzen von CINDs bei der Lösung komplexer Datenqualitätsprobleme aufzeigt. Darüber hinaus definieren wir Qualitätsmaße für Bedingungen basierend auf Sensitivität und Genauigkeit. Wir stellen effiziente Algorithmen vor, die überdeckende und vollständige Bedingungen innerhalb vorgegebener Schwellwerte finden. Unsere Algorithmen wählen nicht nur die Werte der Bedingungen, sondern finden auch die Bedingungsattribute automatisch. Abschließend zeigen wir, dass unser Ansatz effizient sinnvolle und hilfreiche Ergebnisse für den vorgestellten Anwendungsfall liefert.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 62 
KW  - Datenabhängigkeiten
KW  - Bedingte Inklusionsabhängigkeiten
KW  - Erkennen von Meta-Daten
KW  - Linked Open Data
KW  - Link-Entdeckung
KW  - Assoziationsregeln
KW  - Data Dependency
KW  - Conditional Inclusion Dependency
KW  - Metadata Discovery
KW  - Linked Open Data
KW  - Link Discovery
KW  - Association Rule Mining
Y1  - 2012
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-62089
SN  - 978-3-86956-212-4
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  - 
TY  - GEN
A1  - Loster, Michael
A1  - Naumann, Felix
A1  - Ehmueller, Jan
A1  - Feldmann, Benjamin
T1  - CurEx
BT  - a system for extracting, curating, and exploring domain-specific knowledge graphs from text
T2  - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
N2  - 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.
Y1  - 2018
SN  - 978-1-4503-6014-2
U6  - https://doi.org/10.1145/3269206.3269229
SP  - 1883
EP  - 1886
PB  - Association for Computing Machinery
CY  - New York
ER  - 
TY  - JOUR
A1  - Koßmann, Jan
A1  - Papenbrock, Thorsten
A1  - Naumann, Felix
T1  - Data dependencies for query optimization
BT  - a survey
JF  - The VLDB journal : the international journal on very large data bases / publ. on behalf of the VLDB Endowment
N2  - 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.
KW  - Query optimization
KW  - Query execution
KW  - Data dependencies
KW  - Data profiling
KW  - Unique column combinations
KW  - Functional dependencies
KW  - Order dependencies
KW  - Inclusion dependencies
KW  - Relational data
KW  - SQL
Y1  - 2021
U6  - https://doi.org/10.1007/s00778-021-00676-3
SN  - 1066-8888
SN  - 0949-877X
VL  - 31
IS  - 1
SP  - 1
EP  - 22
PB  - Springer
CY  - Berlin ; Heidelberg ; New York
ER  - 
TY  - JOUR
A1  - Hameed, Mazhar
A1  - Naumann, Felix
T1  - Data Preparation
BT  - a survey of commercial tools
JF  - SIGMOD record
N2  - 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.
KW  - data quality
KW  - data cleaning
KW  - data wrangling
Y1  - 2020
U6  - https://doi.org/10.1145/3444831.3444835
SN  - 0163-5808
SN  - 1943-5835
VL  - 49
IS  - 3
SP  - 18
EP  - 29
PB  - Association for Computing Machinery
CY  - New York
ER  - 
TY  - JOUR
A1  - Koumarelas, Ioannis
A1  - Jiang, Lan
A1  - Naumann, Felix
T1  - Data preparation for duplicate detection
JF  - Journal of data and information quality : (JDIQ)
N2  - 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.
KW  - data preparation
KW  - data wrangling
KW  - record linkage
KW  - duplicate detection
KW  - similarity measures
Y1  - 2020
U6  - https://doi.org/10.1145/3377878
SN  - 1936-1955
SN  - 1936-1963
VL  - 12
IS  - 3
PB  - Association for Computing Machinery
CY  - New York
ER  - 
TY  - BOOK
A1  - Abedjan, Ziawasch
A1  - Golab, Lukasz
A1  - Naumann, Felix
A1  - Papenbrock, Thorsten
T1  - Data Profiling
T3  - Synthesis lectures on data management, 52
Y1  - 2019
SN  - 978-1-68173-446-0
PB  - Morgan & Claypool Publishers
CY  - San Rafael
ER  - 
TY  - JOUR
A1  - Actis, M.
A1  - Agnetta, G.
A1  - Aharonian, Felix A.
A1  - Akhperjanian, A. G.
A1  - Aleksic, J.
A1  - Aliu, E.
A1  - Allan, D.
A1  - Allekotte, I.
A1  - Antico, F.
A1  - Antonelli, L. A.
A1  - Antoranz, P.
A1  - Aravantinos, A.
A1  - Arlen, T.
A1  - Arnaldi, H.
A1  - Artmann, S.
A1  - Asano, K.
A1  - Asorey, H. G.
A1  - Baehr, J.
A1  - Bais, A.
A1  - Baixeras, C.
A1  - Bajtlik, S.
A1  - Balis, D.
A1  - Bamba, A.
A1  - Barbier, C.
A1  - Barcelo, M.
A1  - Barnacka, Anna
A1  - Barnstedt, Jürgen
A1  - de Almeida, U. Barres
A1  - Barrio, J. A.
A1  - Basso, S.
A1  - Bastieri, D.
A1  - Bauer, C.
A1  - Becerra Gonzalez, J.
A1  - Becherini, Yvonne
A1  - Bechtol, K. C.
A1  - Becker, J.
A1  - Beckmann, Volker
A1  - Bednarek, W.
A1  - Behera, B.
A1  - Beilicke, M.
A1  - Belluso, M.
A1  - Benallou, M.
A1  - Benbow, W.
A1  - Berdugo, J.
A1  - Berger, K.
A1  - Bernardino, T.
A1  - Bernlöhr, K.
A1  - Biland, A.
A1  - Billotta, S.
A1  - Bird, T.
A1  - Birsin, E.
A1  - Bissaldi, E.
A1  - Blake, S.
A1  - Blanch Bigas, O.
A1  - Bobkov, A. A.
A1  - Bogacz, L.
A1  - Bogdan, M.
A1  - Boisson, Catherine
A1  - Boix Gargallo, J.
A1  - Bolmont, J.
A1  - Bonanno, G.
A1  - Bonardi, A.
A1  - Bonev, T.
A1  - Borkowski, Janett
A1  - Botner, O.
A1  - Bottani, A.
A1  - Bourgeat, M.
A1  - Boutonnet, C.
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T1  - Design concepts for the Cherenkov Telescope Array CTA an advanced facility for ground-based high-energy gamma-ray astronomy
JF  - Experimental astronomy : an international journal on astronomical instrumentation and data analysis
N2  - 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.
KW  - Ground based gamma ray astronomy
KW  - Next generation Cherenkov telescopes
KW  - Design concepts
Y1  - 2011
U6  - https://doi.org/10.1007/s10686-011-9247-0
SN  - 0922-6435
SN  - 1572-9508
VL  - 32
IS  - 3
SP  - 193
EP  - 316
PB  - Springer
CY  - Dordrecht
ER  -