VOODB: A Generic Discrete-Event Random Simulation Model to Evaluate the Performances of OODBs

International audiencePerformance of object-oriented database systems (OODBs) is still an issue to both designers and users nowadays. The aim of this paper is to propose a generic discrete-event random simulation model, called VOODB, in order to evaluate the performances of OODBs in general, and the performances of optimization methods like clustering in particular. Such optimization methods undoubtedly improve the performances of OODBs. Yet, they also always induce some kind of overhead for the system. Therefore, it is important to evaluate their exact impact on the overall performances. VOODB has been designed as a generic discrete-event random simulation model by putting to use a modelling approach, and has been validated by simulating the behavior of the O2 OODB and the Texas persistent object store. Since our final objective is to compare object clustering algorithms, some experiments have also been conducted on the DSTC clustering technique, which is implemented in Texas. To validate VOODB, performance results obtained by simulation for a given experiment have been compared to the results obtained by benchmarking the real systems in the same conditions. Benchmarking and simulation performance evaluations have been observed to be consistent, so it appears that simulation can be a reliable approach to evaluate the performances of OODBs

Modeling and Simulating a Software Architecture Design Space

Frequently, a similar type of software system is used in the implementation of many different software applications. Databases are an example. Two software development approaches are common to Þll the need for instances from a class of similar systems: (1) repeated custom development of similar instances, one for each different application, or (2) development of one or more general purpose off-the-shelf systems that are used many times in the different applications. Each approach has advantages and disadvantages. Custom development can closely match the requirements of an application, but has an associated high development cost. General purpose systems may have a lower cost when amortized across multiple applications, but may not closely match the requirements of all the different applications. It can be difÞcult for application developers to determine which approach is best for their application. Do any of the existing off-the-shelf systems sufÞciently satisfy the application requirements? If so, which ones provide the best match? Would a custom implementation be sufÞciently better to justify the cost difference between an off-the-shelf solution? These difÞcult buy-versus-build decisions are extremely important in todayÕs fastpaced, competitive, unforgiving software application market. In this thesis we propose and study a software engineering approach for evaluating how well off-the-shelf and custom software architectures within the design space of a class of OODB systems satisfy the requirements for different applications. The approach is based on the ability to explicitly enumerate and represent the key dimensions of commonality and variability in the space of OODB designs. We demonstrate that modeling and simulation of OODB software architectures can be used to help software developers rapidly converge on OODB requirements for an application and identify OODB software architectures that satisfy those requirements. The technical focus of this work is on the circular relationships between requirements, software architectures, and system properties such as OODB functionality, size, and performance. We capture these relationships in a parametrized OODB architectural model, together with an OODB simulation and modeling tool that allows software developers to reÞne application requirements on an OODB, identify corresponding custom and offthe- shelf OODB software architectures, evaluate how well the software architecture properties satisfy the application requirements, and identify potential reÞnements to requirements

Generic software for benchmarking formal concept analysis: Orange3 integration

Thanks to the internet of things (IoT) and cyber physical systems (CPS), we face an incremental growth of the available data, either on the internet or in private databases. This resulted in data mining techniques becoming an essential piece in the information retrieval process. Moreover, trends like the industry 4.0 encourages its usage to support data driven decisions, for instance. Formal Concept Analysis (FCA) is one of the most used techniques in the unsupervised data mining field due to its inherent ability to find patterns between concepts. As a consequence, many applications need the use of fast algorithms to perform the calculations to retrieve either the lattice or the association rules related with the data at their disposal. Due to this, scientists often rely on manually crafted benchmarks to compare how certain algorithms perform under different circumstances. In this work, we propose the architecture of a software to generalize these benchmarks independently of the algorithms, to be integrated in the open source data analysis software Orange3.Facultad de Informátic

The advantages and cost effectiveness of database improvement methods

Relational databases have proved inadequate for supporting new classes of applications, and as a consequence, a number of new approaches have been taken (Blaha 1998), (Harrington 2000). The most salient alternatives are denormalisation and conversion to an object-oriented database (Douglas 1997). Denormalisation can provide better performance but has deficiencies with respect to data modelling. Object-oriented databases can provide increased performance efficiency but without the deficiencies in data modelling (Blaha 2000). Although there have been various benchmark tests reported, none of these tests have compared normalised, object oriented and de-normalised databases. This research shows that a non-normalised database for data containing type code complexity would be normalised in the process of conversion to an objectoriented database. This helps to correct badly organised data and so gives the performance benefits of de-normalisation while improving data modelling. The costs of conversion from relational databases to object oriented databases were also examined. Costs were based on published benchmark tests, a benchmark carried out during this study and case studies. The benchmark tests were based on an engineering database benchmark. Engineering problems such as computer-aided design and manufacturing have much to gain from conversion to object-oriented databases. Costs were calculated for coding and development, and also for operation. It was found that conversion to an object-oriented database was not usually cost effective as many of the performance benefits could be achieved by the far cheaper process of de-normalisation, or by using the performance improving facilities provided by many relational database systems such as indexing or partitioning or by simply upgrading the system hardware. It is concluded therefore that while object oriented databases are a better alternative for databases built from scratch, the conversion of a legacy relational database to an object oriented database is not necessarily cost effective

Decisioning 2022 : Collaboration in knowledge discovery and decision making: Applications to sustainable agriculture

Sustainable agriculture is one of the Sustainable Development Goals (SDG) proposed by UN (United Nations), but little systematic work on Knowledge Discovery and Decision Making has been applied to it. Knowledge discovery and decision making are becoming active research areas in the last years. The era of FAIR (Findable, Accessible, Interoperable, Reusable) data science, in which linked data with a high degree of variety and different degrees of veracity can be easily correlated and put in perspective to have an empirical and scientific perception of best practices in sustainable agricultural domain. This requires combining multiple methods such as elicitation, specification, validation, technologies from semantic web, information retrieval, formal concept analysis, collaborative work, semantic interoperability, ontological matching, specification, smart contracts, and multiple decision making. Decisioning 2022 is the first workshop on Collaboration in knowledge discovery and decision making: Applications to sustainable agriculture. It has been organized by six research teams from France, Argentina, Colombia and Chile, to explore the current frontier of knowledge and applications in different areas related to knowledge discovery and decision making. The format of this workshop aims at the discussion and knowledge exchange between the academy and industry members.Laboratorio de Investigación y Formación en Informática Avanzad

A Systematic Performance Study of Object Database Management Systems

Many previous performance benchmarks for Object Database Management Systems (ODBMSs) have typically used arbitrary sets of tests based on what their designers felt were the characteristics of Engineering applications. Increasingly, however, ODBMSs are being used in non-engineering domains, such as Financial Trading, Clinical Healthcare, Telecommunications Network Management, etc. Part of the reason for this is that the technology has matured over the past few years and has become a less risky choice for organisations looking for better w'ays to manage complex data. However, the development of suitable application- or industry-specific benchmarks, based on actual performance studies, has not paralleled this growth. The research reported here approaches performance evaluation of ODBMSs pragmatically. It uses a combination of case studies and benchmark experiments to investigate the performance characteristics of ODBMSs for particular applications, following the successful use of this approach by Youssef [Youss93] for studying the performance of On- Line Transaction Processing (OLTP) applications for Relational Database Management Systems (RDBMSs). Six case studies at five organisations show’ that organisations consider a wide range of factors when undertaking their own performance studies or benchmarks. Furthermore, none of the studied organisations considered using any public benchmarks. Six current and derived benchmarks also highlight statistically significant performance differences between three major commercial products: Objectivity/DB, ObjectStore and UniSQL. These benchmarks indicate the suitability of the products tested for particular application domains. The research could not find any evidence at this time to support the concept of a generic or canonical performance workload for ODBMSs. This is demonstrated by the case studies and supported by the benchmark experiments. However, the research shows that performance benchmarks serve a very useful role in ODBMS evaluations and can help identify architectural and quality problems with products that would not otherwise be observed until significant application or system development was already in progress