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By Xiufeng Liu, Christian Thomsen (auth.), Abdelkader Hameurlain, Josef Küng, Roland Wagner, Alfredo Cuzzocrea, Umeshwar Dayal (eds.)
The LNCS magazine Transactions on Large-Scale information- and Knowledge-Centered structures makes a speciality of facts administration, wisdom discovery, and information processing, that are center and scorching subject matters in computing device technological know-how. because the Nineties, the web has turn into the most motive force at the back of software improvement in all domain names. a rise within the call for for source sharing throughout diversified websites attached via networks has ended in an evolution of information- and knowledge-management platforms from centralized structures to decentralized structures allowing large-scale disbursed purposes supplying excessive scalability. present decentralized structures nonetheless specialize in info and information as their major source. Feasibility of those structures is based primarily on P2P (peer-to-peer) recommendations and the aid of agent platforms with scaling and decentralized keep watch over. Synergy among grids, P2P structures, and agent applied sciences is the most important to facts- and knowledge-centered structures in large-scale environments. This, the 8th factor of Transactions on Large-Scale facts- and Knowledge-Centered platforms, includes 8 revised chosen commonplace papers targeting the next issues: scalable facts warehousing through MapReduce, prolonged OLAP multidimensional versions, naive OLAP engines and their optimization, complex info movement processing and mining, semi-supervised studying of information streams, incremental development mining over information streams, organization rule mining over facts streams, common trend discovery over information streams.
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Extra info for Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII: Special Issue on Advances in Data Warehousing and Knowledge Discovery
Analytics Over Large-scale Multidimensional Data: the Big Data Revolution! In: Proc. of the ACM 14th International Workshop on Data Warehousing and OLAP, pp. 101–104 (2011) 11. : MapReduce: A Flexible Data Processing Tool. CACM 53(1), 72–77 (2010) 12. : MapReduce: Simplified Data Processing on Large Clusters. In: Proc. of OSDI, pp. 137–150 (2004) 13. : Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing). PVLDB 3(1) (2010) 14. : Clustera: An Integrated Computation and Data Management System.
However, they do not offer direct constructs for processing star schemas, snowflaked dimensions, and slowly changing dimensions. In contrast, ETLMR runs separate ETL processes on a MapReduce framework to achieve parallelization and ETLMR directly supports common ETL operations for these dimensional schemas. ETLMR: A Highly Scalable Dimensional ETL Framework Based on MapReduce 23 Another well-known distributed computing system is the parallel DBMS which first appeared two decades ago. , Teradata, DB2, Objectivity/DB, Vertica, etc.
The following three examples illustrate how disaggregation is applied to our data set from Section 3. The Planning OLAP Model – A Multidimensional Model 41 Example 1. In the ﬁrst example we want to distribute a new article quantity of 384 to all sales facts in Germany for year 2011. The input M O contains all ﬁve dimensions Article, Location, SellDate, Price and Quantity and is not restricted. The parameters for the disaggregation are: α−1 [ Article , Germany, 2011, P rice, −1 Quantity , gstd , g = SU M, enew = 384, t = 5, r = 5](M O) −1 The distribution function gstd is the standard function explained in the previous paragraph and the reference aggregate function g is SU M .
Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII: Special Issue on Advances in Data Warehousing and Knowledge Discovery by Xiufeng Liu, Christian Thomsen (auth.), Abdelkader Hameurlain, Josef Küng, Roland Wagner, Alfredo Cuzzocrea, Umeshwar Dayal (eds.)