Hence, we have proposed the dynamic evaluation framework (DEF) that simulates access pattern changes using configurable styles of change. which objects are always accessed in the same order repeatedly. However, all existing benchmarks or evaluation frameworks produce static access patterns in. Changes in access patterns play an important role in determining the efficiency of key performance optimization techniques, such as dynamic clustering, prefetching, and buffer replacement. This chapter explores the effect that changing access patterns has on the performance of database management systems. In this chapter, we instantiate DEF into the dynamic object evaluation framework (DoEF), which is designed for object databases, that is, object-oriented or object-relational databases such as multimedia databases or most eXtensible Mark-up Language (XML) databases. DEF has been designed to be open and fully extensible (e.g., new access pattern change models can also be added easily). Though at an early stage of devel-opment, XWB has been successfully used to test the efficiency of indexing and view materialization techniques in XML data warehouses. XWB is based on an original reference model for XML data warehouses, and proposes a test XML data warehouse and its associated XQuery decision-support workload that are derived from the well-known, rela-tional decision-support benchmark TPC-H. In this paper, we present the XML Warehouse Benchmark (XWB), which aims at filling this gap. are, to the best of our knowledge, no XML decision-support benchmark. Performance in general, and the efficiency of performance optimiza-tion techniques in particular, is usually assessed with the help of benchmarks. To ensure their feasibility, the issue of performance must be addressed. With the emergence of XML as a new standard for representing busi-ness data, new decision-support applications (namely, XML data warehouses) are being developed. Contributions will be reviewed by an international scientific committee. This book plans to gather top-level research contributions addressing problems related to the five "Vs" of big data, technological issues, as well as big data analytics applications. Finally, new technologies such as cloud computing, Hadoop/Spark and NoSQL databases also question classical BI. Finally, actually extracting intelligible information from big data (data value) requires novel methods. Variety and veracity issues remain, but at a much greater extent. Velocity challenges the very idea of materializing historicized data. Data volume challenges even warehouses that were tailored for large amounts of data. With big data coming into play, benefits from processing external data look even better, but issues are also more complex. Yet, tackling data heterogeneity has always been an issue. Mashing up internal and external data is acknowledged as the best way to provide the most complete view for decision making. Finally, we briefly discuss the initial performance results and lessons that resulted from applying BUCKY to one of the early object-relational database system products. To test the maturity of object-relational technology relative to relational technology, we provide both an object-relational version of BUCKY and a relational equivalent thereof (i.e., a relational BUCKY simulation). BUCKY is a query-oriented benchmark that tests many of the key features offered by object-relational systems, including row types and inheritance, references and path expressions, sets of atomic values and of references, methods and late binding, and user-defined abstract data types and their methods. Since we believe that no one should face a revolution without appropriate armaments, this paper presents BUCKY, a new benchmark for object-relational database systems. According to various trade journals and corporate marketing machines, we are now on the verge of a revolution - the object-relational database revolution.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |