2,886 research outputs found
Diamond Dicing
In OLAP, analysts often select an interesting sample of the data. For
example, an analyst might focus on products bringing revenues of at least 100
000 dollars, or on shops having sales greater than 400 000 dollars. However,
current systems do not allow the application of both of these thresholds
simultaneously, selecting products and shops satisfying both thresholds. For
such purposes, we introduce the diamond cube operator, filling a gap among
existing data warehouse operations.
Because of the interaction between dimensions the computation of diamond
cubes is challenging. We compare and test various algorithms on large data sets
of more than 100 million facts. We find that while it is possible to implement
diamonds in SQL, it is inefficient. Indeed, our custom implementation can be a
hundred times faster than popular database engines (including a row-store and a
column-store).Comment: 29 page
On the Evaluation of RDF Distribution Algorithms Implemented over Apache Spark
Querying very large RDF data sets in an efficient manner requires a
sophisticated distribution strategy. Several innovative solutions have recently
been proposed for optimizing data distribution with predefined query workloads.
This paper presents an in-depth analysis and experimental comparison of five
representative and complementary distribution approaches. For achieving fair
experimental results, we are using Apache Spark as a common parallel computing
framework by rewriting the concerned algorithms using the Spark API. Spark
provides guarantees in terms of fault tolerance, high availability and
scalability which are essential in such systems. Our different implementations
aim to highlight the fundamental implementation-independent characteristics of
each approach in terms of data preparation, load balancing, data replication
and to some extent to query answering cost and performance. The presented
measures are obtained by testing each system on one synthetic and one
real-world data set over query workloads with differing characteristics and
different partitioning constraints.Comment: 16 pages, 3 figure
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
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