New Trends in Data Warehousing and Data Analysis (Annals of Information Systems)

New Trends in Data Warehousing and Data Analysis (Annals of Information Systems)

Language: English

Pages: 364

ISBN: 0387874305

Format: PDF / Kindle (mobi) / ePub

New Trends in Data Warehousing and Data Analysis (Annals of Information Systems)

Language: English

Pages: 364

ISBN: 0387874305

Format: PDF / Kindle (mobi) / ePub


Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Today, knowledge-based management systems include data warehouses as their core components. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed to discover trends, patterns of behavior, and anomalies as well as finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data coming from many different sources make data integration and processing challenging.

New Trends in Data Warehousing and Data Analysis brings together the most recent research and practical achievements in the DW and OLAP technologies. It provides an up-to-date bibliography of published works and the resource of research achievements. Finally, the book assists in the dissemination of knowledge in the field of advanced DW and OLAP.

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transportation of data across different heterogeneous databases. CTF solutions move operational data from the sources, apply light-weight transformations, and then, stage the data in a staging area. After that, more complex transformations are applied (triggered by the insertions of data in the staging area) by microbatch ETL and the data are moved to a real time partition and from there, to static data stores in the data warehouse. CTF is a good choice for near real time reporting, with light

completing the processing of all the incoming tuples. Note also that the DPA acts as a flexible buffering area: the input rate of records is not controlled by the DPA administrator, but rather, depends on the characteristics and the workload of the sources, each time; at the same time, the DPA has to guarantee a certain rate of processed records that are ready to be loaded at the warehouse whenever appropriate, necessary, or dictated by the user needs. Therefore, CPU, main memory and storage

, F LOANS }, DBANK = {DCUST OMERS , DDAT ES , DBBRANCHES , DLOANTY PES }, StarBANK : StarBANK (F ACCOUNT S ) → (DDAT ES , DCUST OMERS , DBBRANCHES ), StarBANK (F LOANS ) → (DCUST OMERS , DDAT ES , DLOANTY PES ), • RuleBANK = {R1 , . . . , Rn r}. • • • • 4 Personalization and OLAP Databases 77 fact measure weak attribute hierarchy dimension parameter Fig. 4.4 Example of constellation schema. The dimension noted DCUST OMERS (N DCUST OMERS , ADCUST OMERS , H DCUST OMERS , I DCUST OMERS

processing, TP-Tree codifies a tunable and hierarchical partition of the target data cube, i.e. a partition that varies over time according to the query-workload. As a consequence, TP-Tree is self-adjusting in that a triggerlike procedure updates it, hence updating the underlying partitions according to the 118 Alfredo Cuzzocrea and Wei Wang current query-workload. In such a way, the natural application field of TP-Tree is a middleware system that sits between a DWS and OLAP clients,

algorithm C LOSE [24] in order to perform experimental comparisons between the three representations. We use real data sets to evaluate the validity of our approach. In our experiments, the Quotient Cube is stored by preserving both lower and upper bounds for each class. We use the real dataset SEP85L containing weather conditions at various weather stations or lands from December 1981 through November 19912 . This weather dataset has been frequently used in calibrating various cube algorithms

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