Software

libS4, a C cryptographic library

S4 (Secure Secret Splitting Scheme) implements an additive homomorphic scheme, i.e., additions can be directly computed over encrypted data. S4 is efficient both in terms of storage and computing, without sacrificing privacy too much, which is ideal for data outsourcing scenarios that consider the user has limited computation and storage resources.

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BI4people, business intelligence for all

BI4people is a personal business intelligence prototype system in Software as a Service mode that targets very small companies, organizations and individuals. BI4people aims at providing a simple approach to the decision support process, by masking data integration and warehouse modeling phases, and by allowing a simple navigation through data.

Online access

Contributors : O. Grabova, S. Sobati Moghadam, S. Chagheri, M. Mohammadi Dogahe, H. Benhassan, A. Derrar, M.J. El Ghazi, G. Lachamp

BibTeXWeb, On-line publication list manager with BibTeX support

BibTeXWeb is a PHP on-line publication list manager with BibTeX support. BibTeXWeb helps you manage a database of publications, and display it on your Web page. Furthermore, all the references you enter in your database are available in BibTeX format, either one by one or all at once.

In addition to basic BibTeX data, BibTeXWeb supports abstracts, keywords, various Web links (to publishers, journals, conferences…) and full paper upload/download. BibTeXWeb also features a crude search engine, XML export capabilities (DBLP format), and RSS feed and/or e-mail alerts.

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XML Warehouse Benchmark (XWeB)

XWeB is the first (and only, to the best of our knowledge) XML data warehouse benchmark [ASD06, TPCTC10]. It derives from the relational decision-support benchmark TPC-H. It is mainly composed of a test data warehouse that is based on a unified reference model for XML warehouses and that features XML-specific structures, and its associate XQuery decision-support workload.

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DWEB decision-support benchmark

DWEB logo

DWEB (Data Warehouse Engineering Benchmark) is a benchmark for evaluating the performances of data warehouses [BDA04, DWEB05, DaWaK05, IJBIDM07, ADWM08]. Benchmarking is useful either to system users for comparing the performances of different systems, or to system engineers for testing the effect of various design choices. While the TPC standard decision support benchmarks address the first point, they are not tunable enough to address the second one and fail to model different database schemas. By contrast, DWEB allows to generate various ad-hoc synthetic data warehouses and workloads. DWEB is fully parameterized to fulfill data warehouse design needs. However, two levels of parameterization keep it easy to tune.

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DoEF evaluation framework

DoEF (Dynamic object Evaluation Framework) is a dynamic framework for object database benchmarking [DEXA03, BDA03, ISI04, JDM05, ATDR06]. DoEF accomplishes access pattern change by defining configurable styles of change. It is built on top of the Object Clustering Benchmark (OCB). DoEF is a preliminary prototype that has been designed to be fully extensible. Though originally designed for the object-oriented model (like OCB), it can also be used within the object-relational model with few adaptations. Furthermore, new access pattern change models can be added too. DoEF has been implemented in the VOODB simulation framework and used to compare the performances of four existing state of the art dynamic clustering algorithms (DSTC, DRO, OPCF – graph partitioning, and OPCF – probability ranking principle). The results showed that DoEF is effective at determining theadaptability of each dynamic clustering algorithm to changes in access pattern.

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BuildTree, DBMS-integrated data mining tool

BuildTree [ISMIS02] is a stored procedure written in Oracle PL/SQL that helps building decision trees using the DBMS’ relational structures (namely, views). The result (tree) is also stored as a relational table. It can be queried through Oracle SQL hierarchy queries.

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VOODB simulation model

VOODB (Virtual Object Oriented Database) is a generic discrete-event random simulation model to evaluate the performances of performances of object-oriented database management systems (OODBMSs) in general, and the performances of optimization methods like clustering in particular. VOODB has been designed by applying a modelling methodology [VLDB99] and has been validated by simulating the behavior of the O2 OODBMS and the Texas persistent object store. Experiments concerning the DSTC (Dynamic, Statistical, Tunable Clustering) clistering technique, which is implemented in Texas,have also been performed. To validated VOODB, performances obtained by simulation have been successfully compared to measures done in the same conditions on the real systems.

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DESP-C++ simulation package

DESP-C++ (Discrete Event Simulation Package for C++) is a discrete-event random simulation engine. It has been designed to be fast, very easy to use and expand, and valid. The validity of DESP-C++ has been demonstrated for three significant models [SPE00]. In each case, the simulation results obtained with DESP-C++ were consistent with the results achieved with QNAP2, a valid simulation software. The adapatability of DESP-C++ has also been hinted this way, since the modelled systems were very different from one another: a simple production system, the classical philosophers deadlock problem, and a complex object-oriented database management system.

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OCB benchmark

OCB (Object Clustering Benchmark) is a generic object-oriented benchmark that has been designed to evaluate the performances of object-oriented database management systems (OODBMSs), and more particularly the performances of clustering policies within OODBMSs. OCB is generic thanks to its database, which can be customized to imitate the databases of the main existing benchmarks, like OO1 (Object Operation 1) or OO7. The first version of OCB [EDBT98] was purposely clustering-oriented, but OCB has been completely extended in order to fulfill other objectives [JDS00]. Eventually, OCB’s code is compact and portable. OCB has been validated through two implementations: one within the O2 OODBMS and the other within the Texas persistent object store. The performances of two specific clustering policies named DSTC (Dynamic, Statistical, Tunable Clustering) and DRO (Detection & Reclustering of Objects) have also been evaluated with OCB.

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