Special Issue "Data Modeling for Big Data Analytics"

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 December 2018).

Special Issue Editors

Prof. Dr. Matteo Golfarelli
Website
Guest Editor
Department of Computer Science, University of Bologna - Via Zamboni, 33-40126 Bologna, Italy
Interests: business intelligence; big data; big data analytics, noSQL, data mining; machine learning; social business intelligence; trajectory data; trajectory mining; precision farming
Prof. Stefano Rizzi
Website
Guest Editor
DISI – Univ. of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
Interests: data warehouse design; business intelligence; big data

Special Issue Information

Dear Colleagues,

Business intelligence (BI) applications are data-intensive; thus, data modeling is a key aspect to ensure their effectiveness and efficiency. At the conceptual level, data modeling provides a high level of abstraction in describing the structure and features of the information to be delivered in an implementation-independent way. At the logical and physical levels, it enables data structures to be specifically fine-tuned to achieve good performances on the target data model. Since the 1990s, data modeling for BI has mainly meant multidimensional modeling, which has been a key to access the benefits of OLAP [DM1] querying in data warehouses. Nowadays, the increase in analytics and big data technologies asks for a review and extension of the classical multidimensional paradigm and paves the way to new solutions tailored for the emerging user needs and for the specific technological features of big data platforms. In this context, we seek for original submissions that contribute novel approaches, solutions, methods, languages, and applications on the following topics:

  • Data modeling in the context of conceptual design of data warehouses, analytics, and business intelligence applications, in presence of big data sources
  • Data modeling in the context of logical design of data warehouses, analytics, and business intelligence applications, when the target platform is a big data one
  • Design methodologies related to the above mentioned data models
  • Techniques for efficiently querying and accessing the above mentioned data models
Prof. Matteo Golfarelli
Prof. Stefano Rizzi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Foundations and Concept Formalization
  • Domain-Specific Models and Methods
  • Methodologies and Tools
  • Quality and Metrics
  • Evolution
  • Metadata and Applications
  • Spatio-Temporal Aspects
  • Modeling of Stream and Sensor Data
  • Empirical Studies
  • NoSQL and NewSQL Databases
  • Big Data Analytics

Published Papers (2 papers)

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Research

Open AccessArticle
Domain-Specific Aspect-Sentiment Pair Extraction Using Rules and Compound Noun Lexicon for Customer Reviews
Informatics 2018, 5(4), 45; https://doi.org/10.3390/informatics5040045 - 29 Nov 2018
Cited by 1
Abstract
Online reviews are an important source of opinion to measure products’ quality. Hence, automated opinion mining is used to extract important features (aspect) and related comments (sentiment). Extraction of correct aspect-sentiment pairs is critical for overall outcome of opinion mining; however, current works [...] Read more.
Online reviews are an important source of opinion to measure products’ quality. Hence, automated opinion mining is used to extract important features (aspect) and related comments (sentiment). Extraction of correct aspect-sentiment pairs is critical for overall outcome of opinion mining; however, current works still have limitations in terms of identifying special compound noun and parent-child relationship aspects in the extraction process. To address these problems, an aspect-sentiment pair extraction using the rules and compound noun lexicon (ASPERC) model is proposed. The model consists of three main phases, such as compound noun lexicon generation, aspect-sentiment pair rule generation, and aspect-sentiment pair extraction. The combined approach of rules generated from training sentences and domain specific compound noun lexicon enable extraction of more aspects by firstly identifying special compound noun and parent-child aspects, which eventually contribute to more aspect-sentiment pair extraction. The experiment is conducted with the SemEval 2014 dataset to compare proposed and baseline models. Both ASPERC and its variant, ASPER, result higher in recall (28.58% and 22.55% each) compared to baseline and satisfactorily extract more aspect sentiment pairs. Lastly, the reasonable outcome of ASPER indicates applicability of rules to various domains. Full article
(This article belongs to the Special Issue Data Modeling for Big Data Analytics)
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Open AccessArticle
Modeling Analytical Streams for Social Business Intelligence
Informatics 2018, 5(3), 33; https://doi.org/10.3390/informatics5030033 - 01 Aug 2018
Cited by 5
Abstract
Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous [...] Read more.
Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector. Full article
(This article belongs to the Special Issue Data Modeling for Big Data Analytics)
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