In-Memory Business Intelligence: Concepts and Performance

Rantung, Vivi Peggie; Kembuan, Olivia; Rompas, Parabelem Tinno Dolf; Mewengkang, Alfrina; Liando, Olivia Eunike Selvie; Sumayku, James J. R.

doi:10.1088/1757-899x/306/1/012129

IOP Conf. Ser.: Mater. Sci. Eng.

2018

DOI: 10.1088/1757-899x/306/1/012129

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In-Memory Business Intelligence: Concepts and Performance

Vivi Peggie Rantung

Olivia Kembuan

Parabelem Tinno Dolf Rompas

et al.

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“…A field in which this approach could appear paradoxical is Business Intelligence. However, there exist systems (see [42]) that perform Business Intelligence tasks on large volumes of data to aggregate in main memory. The Qlik suite [43,44] is a quite famous suite for Business Intelligence that operates in main memory: it first collects data from data sources into main memory, then it performs aggregations requested by users.…”

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J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

Psaila

Fosci

2021

Electronics

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Internet technology and mobile technology have enabled producing and diffusing massive data sets concerning almost every aspect of day-by-day life. Remarkable examples are social media and apps for volunteered information production, as well as Open Data portals on which public administrations publish authoritative and (often) geo-referenced data sets. In this context, JSON has become the most popular standard for representing and exchanging possibly geo-referenced data sets over the Internet.Analysts, wishing to manage, integrate and cross-analyze such data sets, need a framework that allows them to access possibly remote storage systems for JSON data sets, to retrieve and query data sets by means of a unique query language (independent of the specific storage technology), by exploiting possibly-remote computational resources (such as cloud servers), comfortably working on their PC in their office, more or less unaware of real location of resources. In this paper, we present the current state of the J-CO Framework, a platform-independent and analyst-oriented software framework to manipulate and cross-analyze possibly geo-tagged JSON data sets. The paper presents the general approach behind the J-CO Framework, by illustrating the query language by means of a simple, yet non-trivial, example of geographical cross-analysis. The paper also presents the novel features introduced by the re-engineered version of the execution engine and the most recent components, i.e., the storage service for large single JSON documents and the user interface that allows analysts to comfortably share data sets and computational resources with other analysts possibly working in different places of the Earth globe. Finally, the paper reports the results of an experimental campaign, which show that the execution engine actually performs in a more than satisfactory way, proving that our framework can be actually used by analysts to process JSON data sets.

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J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

Psaila

Fosci

2021

Electronics

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Using Business Analytics for Decision Support in Zero Defect Manufacturing of Composite Parts in the Aerospace Industry

et al. 2019

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In-Memory Business Intelligence: Concepts and Performance

Cited by 2 publications

References 8 publications

J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

Using Business Analytics for Decision Support in Zero Defect Manufacturing of Composite Parts in the Aerospace Industry

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