Paper Info
Title
Towards Semantic Assessment of Summarizability in Self-service Business Intelligence.
Abstract
Traditional Business Intelligence solutions allow decision makers to query multidimensional data cubes by using OLAP tools, thus ensuring summarizability, which refers to the possibility of accurately computing aggregation of measures along dimensions. With the advent of the Web of Open Data, new external sources have been used in Self-service Business Intelligence for acquiring more insights through ad-hoc multidimensional open data cubes. However, as these data cubes rely upon unknown external data, decision makers are likely to make meaningless queries that lead to summarizability problems. To overcome this problem, in this paper, we propose a framework that automatically extracts multidimensional elements from SPARQL query logs and creates a knowledge base to detect semantic correctness of summarizability.
Year
Venue
Field
2017
ADBIS (Short Papers and Workshops)
Open data,Self-service,Information retrieval,Computer science,Correctness,SPARQL,Knowledge base,Online analytical processing,Business intelligence,Database,Data cube
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
10
3
Name
Order
Citations
PageRank
Luis Daniel Ibáñez1266.71
Jose-Norberto Mazón276356.29
Elena Simperl31069122.60