OLAP textual aggregation approach using the Google similarity distance

Bouakkaz, Mustapha; Loudcher, Sabile; Ouinten, Youcef

doi:10.1504/ijbidm.2016.076425

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…Those BI characteristics help a company to support change, competition, decisions or events (Li et al, 2008a;Petrini and Pozzebon, 2009;Perko et al, 2011;Işık et al, 2013). Hybrid measurements (HMS) use analytical processes, algorithms (Perko et al, 2011), surveys, web analysis (Zorrilla and García-Saiz, 2013), paper reports, ad hoc reports, OLAP (Bouakkaz et al, 2016), data mining, dashboards, KPIs, alerts, (Popovič et al, 2012) reports, dashboards and scorecards, ad hoc querying (Vujošević et al, 2012), financial databases (Cheng et al, 2009) or CRM (Phan and Vogel, 2010). BI is also characterised as a non-financial measurement tool, which supports a firm by using multi-dimensional and unstructured data (Gao andXu, 2009, Chaudhuri et al, 2011).…”

Section: Micro Level Of Bimentioning

confidence: 99%

Technology in its Context

Fischer

2017

IJBIDM

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The purpose of the literature review is to identify characteristics, concepts, and theories of business intelligence (BI). The status quo of BI is based on the literature review, which covers 86 journal articles from the three areas of accounting, strategy, and information systems between 2006 and 2014. The review combines two established frameworks to illustrate new insights regarding the macro and micro levels of BI. The complementary combination of both levels produces a new lens that shows the conceptualisation and characteristics of BI in a holistic view. The result of the study shows that BI is used as a monolithic concept and static tool with technical control mechanisms. Another result implies that BI is in a phase of maturity, in which it fulfils an organisational purpose without considering its social context or ecosystem in which it occurs. The literature review contributes to the characterisation and theorisation of BI and shows that a company depend on both characteristics and the purpose for which BI is used.

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Section: Micro Level Of Bimentioning

confidence: 99%

Technology in its Context

Fischer

2017

IJBIDM

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“…For event detection [17], term weights are used to measure the appearance of bursty topic in Online Social Networks. Trend discovery [6,7,34] employs Text Cubing and Online Analytical Processing (OLAP) [54,55] to construct term weighting schemes, which are used for analyzing the impact of products in Social Media. Sentiment analysis uses weighting schemes to vectorize textual data before detecting the polarity of each document [30].…”

Section: Introductionmentioning

confidence: 99%

TextBenDS: a Generic Textual Data Benchmark for Distributed Systems

et al. 2020

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Extracting top-k keywords and documents using weighting schemes are popular techniques employed in text mining and machine learning for different analysis and retrieval tasks. The weights are usually computed in the data preprocessing step, as they are costly to update and keep track of all the modifications performed on the dataset. Furthermore, computation errors are introduced when analyzing only subsets of the dataset. Therefore, in a Big Data context, it is crucial to lower the runtime of computing weighting schemes, without hindering the analysis process and the accuracy of the machine learning algorithms. To address this requirement for the task of top-k keywords and documents, it is customary to design benchmarks that compare weighting schemes within various configurations of distributed frameworks and database management systems. Thus, we propose a generic document-oriented benchmark for storing textual data and constructing weighting schemes (TextBenDS). Our benchmark offers a generic data model designed with a multidimensional approach for storing text documents. We also propose using aggregation queries with various complexities and selectivities for constructing term weighting schemes, that are utilized in extracting top-k keywords and documents. We evaluate the computing performance of the queries on several distributed environments set within the Apache Hadoop ecosystem. Our experimental results provide interesting insights. As an example, MongoDB proves to have the best overall performance, while Spark's execution time remains almost the same, regardless of the weighting schemes.

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“…Este cenário dificulta muito o trabalho de gestores, executivos e analistas, uma vez que, segundo [20], a quantidade excessiva de documentos excedeu em muito a capacidade humana de compreensão dos dados. Estes obstáculos de análise iniciam a nossa demanda para a remodelagem das ferramentas de Online Analytical Processing (OLAP) tradicionais, uma vez que as mesmas se mostram inadequados para o tipo de dado textual [2]. O operador cubo de dados [6], núcleo de qualquer ferramenta OLAP, enfrenta novos desafios ou impossibilidades quando aplicado à bases de dados multidimensionais textuais.…”

Section: Introductionunclassified

OLAP Textual com Múltiplas Hierarquias de Tópicos e Rankings Segmentados

Souza

Fortes

Lima

2017

Anais Do Simpósio Brasileiro De Sistemas De Informação (SBSI)

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RESUMONa última década a tecnologia OLAP tem sido redesenhada para melhor atender à demanda de dados textuais, tendo que remodelar suas medidas e dimensões. A hierarquia de tópicos surgiu como uma alternativa para organizar dimensões textuais em diferentes níveis semânticos. Contudo, tal hierarquia é criada uma única vez e utilizada para todos os cuboides do cubo. A hierarquia de tópicos é sensível ao conteúdo dos documentos, portanto diferentes células de um cubo agregam diferentes conjuntos de documentos, produzindo hierarquias de tópicos distintas. Este artigo apresenta uma abordagem para OLAP textual que constrói múltiplas hierarquias de tópicos para cada célula do cubo, denominada DTCubing. Múltiplas hierarquias são viáveis porque cada documento pode ser particionado em diversos segmentos de texto, tais como título, resumo, parágrafo, dentre outros. Este artigo também pretende contribuir com a apresentação dos resultados das consultas multidimensionais. O estado da arte em OLAP textual normalmente retorna os top-k documentos mais relevantes como resultado de suas consultas. A abordagem DTCubing vai além, retornando também os top-k segmentos de texto mais relevantes, portanto os parágrafos e resumos mais relevantes podem ser retornados. Os experimentos realizados utilizando artigos indexados pela DBLP confirmam as hipóteses do trabalho. ABSTRACTIn the last decade, the OLAP technology has been redesigned for the textual data, therefore dimensions, hierarchies and measures are being remodeled. Topic hierarchy is a useful alternative to organize document collections. Currently, the topic hierarchy is defined only once in the data cube, i.e., for the entire lattice of cuboids. However, textual hierarchy is sensitive to the content of the documents. Thus, a data cube cell can contain a collection of documents distinct from others in the same cube, since they have complementary aggregation levels that potentially introduce changes in the topic hierarchy. In this paper, we present a textual OLAP approach, named DTCubing, which handles multiple topic hierarchies for each cube cell. Multiple hierarchies are feasible because a document can be partitioned into several text segments (e.g., title, abstract, keywords and many more). A second contribution of this paper refers to query response. The state of art in textual OLAP normally returns the top-k documents as a query result. We go beyond by returning other text segments, such as the most significant titles, abstracts and paragraphs. Experiments, using part of the DBLP papers, reinforce our assumptions. CCS Concepts• Information systems ➝ Data management systems ➝ Database management system engines ➝ Online analytical processing engine.

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OLAP textual aggregation approach using the Google similarity distance

Cited by 10 publications

References 29 publications

Technology in its Context

Technology in its Context

TextBenDS: a Generic Textual Data Benchmark for Distributed Systems

OLAP Textual com Múltiplas Hierarquias de Tópicos e Rankings Segmentados

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