Data management with SAPs in-memory computing engine

Boese, Joos-Hendrik; Tosun, Cafer; Mathis, Christian; Faerber, Franz

doi:10.1145/2247596.2247661

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…However, our PostgreSQL installation can neither leverage pipeline parallelism nor modern multicore hardware. Moreover, most text data sets are not web-scale and will fit in a compressed format in a main memory database [9]. In our future work we will explore the power of such database systems for domain adaption tasks on data sets with the sample size N=all.…”

Section: Discussionmentioning

confidence: 99%

Indrex

Kilias

Löser

Andritsos

2013

Proceedings of the Sixteenth International Workshop on Data Warehousing and OLAP

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Relation extraction transforms the textual representation of a relationship into the relational model of a data warehouse. Early systems, such as SystemT by IBM or the open source system GATE solve this task with handcrafted rule sets that the system executes document-by-document. Thereby the user must execute a highly interactive and iterative process of reading a document, of expressing rules, of testing these rules on the next document and of refining rules. Until now, these systems do neither leverage the full potential of built-in declarative query languages nor the indexing and query optimization techniques of a modern RDBMS that would enable a user interactive rule refinement across documents and on the entire corpus. We propose the INDREX system that enables a user for the first time to describe corpus-wide extraction tasks in a declarative language and permits the user to run interactive rule refinement queries. For enabling this powerful functionality we extend a standard PostgreSQL with a set of white-box user-definedfunctions that enable corpus-wide transformations from sentences into relationships . We store the text corpus and rules in the same RDBMS that already holds domain specific structured data. As a result, (1) the user can leverage this data to further adapt rules to the target domain, (2) the user does not need an additional system for rule extraction and (3) the INDREX system can leverage the full power of built-in indexing and query optimization techniques of the underlaying RDBMS. In a preliminary study we report on the feasibility of this disruptive approach and show multiple queries in INDREX on the Reuters Corpus, Volume 1.

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Section: Discussionmentioning

confidence: 99%

Indrex

Kilias

Löser

Andritsos

2013

Proceedings of the Sixteenth International Workshop on Data Warehousing and OLAP

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“…In addition to this querying process, we have to take into account the fact that both data and users' analysis needs could evolve, requiringanevolutionofthedatawarehouse modeland/oranenrichmentofthedatawarehouse.Tocopewiththeevolutionofthedata themselves,thesolutionoftemporalmodeling and design of DWs should be considered as explainedinMalinowskiandZimányi (2008) To conclude this section, we note that, although performance and scalability of the envisioned approach are clearly out of the scopeofthispaper,webelievethatourvision will benefit from today's technology (Boese et al,2012).…”

Section: Answer Processingmentioning

confidence: 99%

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

Aufaure

Cuzzocrea

Favre

et al. 2013

International Journal of Data Warehousing and Mining

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In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

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Combined Search in Structured and Unstructured Medical Data

Heller¹

2013

High-Performance in-Memory Genome Data Analysis

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Data management with SAPs in-memory computing engine

Cited by 3 publications

References 3 publications

Indrex

Indrex

An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

Combined Search in Structured and Unstructured Medical Data

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