TY  - CHAP
A1  - Fan, Yang
A1  - Masuhara, Hidehiko
A1  - Aotani, Tomoyuki
A1  - Nielson, Flemming
A1  - Nielson, Hanne Riis
T1  - AspectKE*: Security aspects with program analysis for distributed systems
N2  - Enforcing security policies to distributed systems is difficult, in particular, when a system contains untrusted components. We designed AspectKE*, a distributed AOP language based on a tuple space, to tackle this issue. In AspectKE*, aspects can enforce access control policies that depend on future behavior of running processes. One of the key language features is the predicates and functions that extract results of static program analysis, which are useful for defining security aspects that have to know about future behavior of a program. AspectKE* also provides a novel variable binding mechanism for pointcuts, so that pointcuts can uniformly specify join points based on both static and dynamic information about the program. Our implementation strategy performs fundamental static analysis at load-time, so as to retain runtime overheads minimal. We implemented a compiler for AspectKE*, and demonstrate usefulness of AspectKE* through a security aspect for a distributed chat system.
KW  - aspect oriented programming
KW  - program analysis
KW  - security policies
KW  - distributed systems
KW  - tuple spaces
Y1  - 2010
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-41369
ER  - 
TY  - JOUR
A1  - Kaitoua, Abdulrahman
A1  - Rabl, Tilmann
A1  - Markl, Volker
T1  - A distributed data exchange engine for polystores
JF  - Information technology : methods and applications of informatics and information technology
JF  - Information technology : Methoden und innovative Anwendungen der Informatik und Informationstechnik
N2  - There is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary.
Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines' instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30 % for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines.
KW  - distributed systems
KW  - data migration
KW  - data transformation
KW  - big data
KW  - engine
KW  - data integration
Y1  - 2020
U6  - https://doi.org/10.1515/itit-2019-0037
SN  - 1611-2776
SN  - 2196-7032
VL  - 62
IS  - 3-4
SP  - 145
EP  - 156
PB  - De Gruyter
CY  - Berlin
ER  -