Combining OLAP and information networks for bibliographic data analysis: a survey

Models of multi-dimensional analysis for qualitative data and its application

SummaryThe Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.

Many modern applications, including online financial feeds, tag-based mass transit systems and RFID-based supply chain management systems transmit real-time data streams. There is a need for a special-purpose event stream processing technology to analyze this vast amount of sequential multi-dimensional data to enable online, operational decision making. Existing techniques such as traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while state-of-the-art Complex Event Processing (CEP) systems designed for sequence detection do not support OLAP operations. Supporting complex pattern queries at different concept and pattern hierarchies must be devised by providing efficient computation and data sharing. In this dissertation, we propose a novel E-Cube model that combines CEP and OLAP techniques for multi-dimensional event pattern analysis at different abstraction levels. Further, we go beyond the linear sequence pattern queries targeted by ECube core system towards supporting an expressive composite pattern query language (composed of arbitrarily nested sequence, negation, recursion, AND and OR operators) to express powerful pattern matching requests.

Business Intelligence (BI) refers to skills, processes, technologies, applications and practices used to support decision making. BI technologies provide historical, current, and predictive views of business operations which are normally used to analyze business data. Online Analytical Processing (OLAP) is one of the common BI approaches in quickly answering multidimensional analytical queries for analytical purpose. In this paper, we have proposed OLAP technique to be implemented in Academic area. Through this technique, UniSZA students’ academic pattern behaviors can be analyzed. A set of data from students’ examination results in relational DB is extracted into multi-dimensional model to support OLAP query processing. The results are grouped into several subject areas. Then, the analysis to recognize students’ academic pattern behaviors is conducted. From the analysis, the groups of students who have the excellent skills or vice versa can be identified. It also optimizes the time dimension to perform current and historical data analysis. The weaknesses and strengths of the student can also be obtained. Finally, students’ future potential areas can be predicted for the next level of educations.

Business intelligence encompasses all activities dealing with collecting, storing/managing, and analyzing business-relevant data with the objective of generating knowledge as input to decision support. Business intelligence is often used as an umbrella term for data warehousing, reporting and OLAP (online analytical processing), MIS/DSS, and data mining, respectively.If we count all topics listed above, it is obvious that business intelligence has quite a long history also in the tourism domain. As early examples in tourism, we can identify the DINAMO system introduced by American Airlines already in 1988 or TourMIS in 1998.The widespread use of ICT, especially the uptake of the Internet and social media, led to an increase of available data on customers, competitors, and the whole market in all major business domains, including tourism. More powerful hardware and sophisticated methods to store and analyze such data turned business intelligence into one of the fastest-growing technologies and most challenging areas in the last decade.This chapter gives an overview on the topic of business intelligence and all technical components of a BI architecture (i.e., information extraction and transformation, data warehousing, and different mechanisms and tools to access and analyze data, like reporting or OLAP tools, dashboards, or data mining toolsets). Moreover, the chapter looks at the history of BI in tourism and presents and discusses typical application scenarios in tourism. Finally, we look at current trends and latest developments in the area of business intelligence and their expected implications for the tourism domain.KeywordsBusiness intelligenceData warehousingData analysisOnline analytical processingManagement information systemsDecision support systems

The industrial use of open source Business Intelligence (BI) tools is becoming more common, but is still not as widespread as for other types of software. It is therefore of interest to explore which possibilities are available for open source BI and compare the tools. In this survey article, we consider the capabilities of a number of open source tools for BI. In the article, we consider a number of Extract-Transform-Load (ETL) tools, database management systems (DBMSs), On-Line Analytical Processing (OLAP) servers, and OLAP clients. We find that, unlike the situation a few years ago, there now exist mature and powerful tools in all these categories. However, the functionality still falls somewhat short of that found in commercial tools.

The traditional OLAP (On-Line Analytical Processing) systems store data in relational databases. Unfortunately, it is difficult to manage big data volumes with such systems. As an alternative, NoSQL systems (Not-only SQL) provide scalability and flexibility for an OLAP system. We define a set of rules to map star schemas and its optimization structure, a precomputed aggregate lattice, into two logical NoSQL models: column-oriented and document-oriented. Using these rules we analyse and implement two decision support systems, one for each model (using MongoDB and HBase).We compare both systems during the phases of data (generated using the TPC-DS benchmark) loading, lattice generation and querying.

Studies of bibliographic data suggest a strong correlation between the growth of citation networks and their corresponding co-authorship networks. We explore the interdependence between evolving citation and co-authorship networks focused on the publications, by Indian authors, in American Physical Society journals between 1970 and 2013. We record interactions between each possible pair of authors in two ways: first, by tracing the change in citations they exchanged and, second, by tracing the shortest path between authors in the co-authorship network. We create these data for every year of the period of our analysis. We use probability methods to quantify the correlation between citations and shortest paths, and the effect on the dynamics of the citation-co-authorship system. We find that author pairs who have a co-authorship distance $d \leq 3$ significantly affect each others citations, but that this effect falls off rapidly for longer distances in the co-authorship network. The exchange of citation between pairs with $d=1$ exhibits a sudden increase at the time of first co-authorship events and decays thereafter, indicating an aging effect in collaboration. This suggests that the dynamics of the co-authorship network appear to be driving those of the citation network rather than vice versa. Moreover, the majority of citations received by most authors are due to reciprocal citations from current, or past, co-authors. We conclude that, in order to answer questions on nature and dynamics of scientific collaboration, it is necessary to study both co-authorship and citation network simultaneously.

With the ever more rapid development of information technology, computer and network infrastructures have become an indispensable part of modern life and work. In this evolving context, the importance of computer-assisted information analysis is increasingly evident, highlighting the need for a solid framework to support its further development. This paper attempts to establish a comprehensive, multi-dimensional technological framework to improve capacity in this crucial area. The proposed framework covers core technology areas such as databases, data warehouses, online transaction processing (OLTP), business intelligence (BI), online analytical processing (OLAP) and decision support systems (ss). By combining these fundamentals with cutting-edge technologies, including data extraction, social network analysis, and massive data analysis and advanced online analytical processing, the framework aims to create a holistic system for computer-aided information analysis. The system aims not only to optimize the efficiency and effectiveness of data processing and analysis, but also to promote innovation in the way information is used for decision-making and strategic planning, contributing to the continued development and impact of all industries.

This paper proposes a visualization system for getting insight into future research activities from co-authorship networks. A bibliographic network such as a co-authorship network and a citation network is important information for researchers when doing a research survey. In particular, there are many requests on research survey that relate with researchers' future activities, such as identification of remarkable of researchers including growing researchers and supervisors. Although a citation network has received many attentions from researchers, it is not suitable for such surveys because it reflects researchers' past activities. Since collaboration of researchers is essential for researchers' activities, co-authorship network is suitable for predicting future activities. In order to get insights into future research activities by discriminating growing research areas from grown-up areas, the proposed visualization system provides the function for identifying research areas and that for identifying time variation of both network structure and keyword distribution. As a basis for getting insights into future research activities, this paper focuses on the task of discriminating growing researchers from supervisors. The effectiveness of the proposed system is evaluated through the detailed analysis of two participants' analyzing process of InfoVis 2004 Contest dataset.

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote $\mathcal{L}_t,$ able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet.

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

In the context of corporate business intelligence, the use of OLAP (On-Line Analytical Processing) tools constitutes a fundamental step to improve decision making. To help systematization and capture of reasoning during decision processes, this paper describes a computational environment to complement OLAP tools functionality, called AMPA (the initials for Analytical Processing Memory Environment, in Portuguese). AMPA proposes a structured representation of decision processes, supporting the retention of decision instances in organizational memory. This environment includes a series of sub-process components that guide the execution of decision processes (represented as frameworks) and describe many of their common activities (represented as beans), especially focusing on typical OLAP operations.

Radio Frequency Identification (RFID) technology is used in many applications for monitoring object movement. The use of RFID in supply chain management systems enables to track the movement of products from suppliers to warehouses, store backrooms, and eventually points of sale. The vast amount of data resulting from the proliferation of RFID readers and tags poses challenges for data management and analytics. RFID data warehousing can enhance data quality and consistency, and give great potential benefits for Online Analytical Processing (OLAP) applications. Traditional data warehouses are built primarily on relational database management systems. However, the size of RFID data being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hive is an open-source data warehousing solution built on top of Hadoop which is a popular Big Data computing framework. This paper presents alternative RFID data warehouse designs which can handle a large amount of RFID data and support a variety of OLAP queries. The proposed approaches are implemented on Hive and evaluated for query performance in cloud computing environment.