Multidimensional Analysis of Big Data

2019 ◽  
pp. 198-224
Author(s):  
Salman Ahmed Shaikh ◽  
Kousuke Nakabasami ◽  
Toshiyuki Amagasa ◽  
Hiroyuki Kitagawa
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Multidimensional Analysis ◽  
Materialized Views ◽  
Data Generation ◽  
Interactive Analysis ◽  
On Line ◽  
Analytical Processing ◽  
Tools And Techniques ◽  
Processing Engine

Data warehousing and multidimensional analysis go side by side. Data warehouses provide clean and partially normalized data for fast, consistent, and interactive multidimensional analysis. With the advancement in data generation and collection technologies, businesses and organizations are now generating big data (defined by 3Vs; i.e., volume, variety, and velocity). Since the big data is different from traditional data, it requires different set of tools and techniques for processing and analysis. This chapter discusses multidimensional analysis (also known as on-line analytical processing or OLAP) of big data by focusing particularly on data streams, characterized by huge volume and high velocity. OLAP requires to maintain a number of materialized views corresponding to user queries for interactive analysis. Precisely, this chapter discusses the issues in maintaining the materialized views for data streams, the use of special window for the maintenance of materialized views and the coupling issues of stream processing engine (SPE) with OLAP engine.

Big Data

2017 ◽  
pp. 158-179
Author(s):  
Vinod Kumar ◽  
Ramjeevan Singh Thakur
Keyword(s):  
Big Data ◽  
Database System ◽  
Smart Phones ◽  
Data Handling ◽  
Data Generation ◽  
Business Organizations ◽  
Nosql Databases ◽  
Data Formats ◽  
Nosql Database ◽  
Tools And Techniques

With every passing day, data generation is increasing exponentially, its volume, variety, velocity are making it quite challenging to analyze, interpret, visualize for gaining the greater insights from the available data. Billions of networked sensors are being embedded in devices such as smart phones, automobiles, social media sites, laptop, PC's and industrial machines etc. that operates, generate and communicate data. Thus, the data obtained from various resources exists in structured, semi-structured and unstructured form. The traditional database system is not suitable to handle these data formats. Therefore, new tools and techniques are developed to work with these data. NoSQL is one of them. Currently, many NoSQL database are available in the market, each one of them specially designed to solve specific type of data handling problems, most of the NoSQL databases are developed with special attention to problem of business organizations and enterprises. The chapter focuses various aspects of NoSQL as tool for handling the big data.

Multidimensional Anlaysis of XML Document Contents with OLAP Dimensions

2011 ◽  
pp. 155-171
Author(s):  
Franck Ravat ◽  
Olivier Teste ◽  
Ronan Tournier
Keyword(s):  
Decision Maker ◽  
Data Warehousing ◽  
Multidimensional Analysis ◽  
Data Format ◽  
Document Structure ◽  
Textual Data ◽  
Xml Document ◽  
On Line ◽  
Analytical Processing ◽  
Numeric Data

With the emergence of Semi-structured data format (such as XML), the storage of documents in centralised facilities appeared as a natural adaptation of data warehousing technology. Nowadays, OLAP (On-Line Analytical Processing) systems face growing non-numeric data. This chapter presents a framework for the multidimensional analysis of textual data in an OLAP sense. Document structure, metadata, and contents are converted into subjects of analysis (facts) and analysis axes (dimensions) within an adapted conceptual multidimensional schema. This schema represents the concepts that a decision maker will be able to manipulate in order to express his analyses. This allows greater multidimensional analysis possibilities as a user may gain insight within a collection of documents.

Materialized View Selection for Data Warehouse Design

2011 ◽  
pp. 1182-1187 ◽  
Author(s):  
Dimitri Theodoratos ◽  
Wugang Xu ◽  
Alkis Simitsis
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Information Sources ◽  
Distributed Databases ◽  
Materialized Views ◽  
Materialized View ◽  
Other Information ◽  
Materialized View Selection ◽  
On Line ◽  
Analytical Processing

A Data Warehouse (DW) is a repository of information retrieved from multiple, possibly heterogeneous, autonomous, distributed databases and other information sources for the purpose of complex querying, analysis and decision support. Data in the DW are selectively collected from the sources, processed in order to resolve inconsistencies, and integrated in advance (at design time) before data loading. DW data are usually organized multidimensionally to support On-Line Analytical Processing (OLAP). A DW can be abstractly seen as a set of materialized views defined over the source relations. During the initial design of a DW, the DW designer faces the problem of deciding which views to materialize in the DW. This problem has been addressed in the literature for different classes of queries and views and with different design goals.

A Survey of Big Data Analytics Systems

2014 ◽  
pp. 392-418 ◽  
Author(s):  
M. Baby Nirmala
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Processing System ◽  
Full Potential ◽  
Analytical Processing ◽  
Tools And Techniques ◽  
New Generation ◽  
Analytical Platforms

In this emerging era of analytics 3.0, where big data is the heart of talk in all sectors, achieving and extracting the full potential from this vast data is accomplished by many vendors through their new generation analytical processing systems. This chapter deals with a brief introduction of the categories of analytical processing system, followed by some prominent analytical platforms, appliances, frameworks, engines, fabrics, solutions, tools, and products of the big data vendors. Finally, it deals with big data analytics in the network, its security, WAN optimization tools, and techniques for cloud-based big data analytics.

scholarly journals CREATING STRATEGIC BUSINESS VALUE FROM BIG DATA ANALYSIS – APPLICATION TELECOM NETWORK DATA AND PLANNING DOCUMENTS

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 691-695
Author(s):  
O. Yazidi Alaoui ◽  
S. Hamdoune ◽  
H. Zili ◽  
H. Boulassal ◽  
M. Wahbi ◽  
...  
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Satellite Image ◽  
Digital Terrain Model ◽  
Multidimensional Analysis ◽  
Terrain Model ◽  
Digital Terrain ◽  
Analysis Application ◽  
On Line ◽  
Analytic Processing

Abstract. Mobile networks carrier gather and accumulate in their database system a considerable volume of data, that carries geographic information which is crucial for the growth of the company. This work aimed develop a prototype called Spatial On -Line Analytic Processing (SOLAP) to carry out multidimensional analysis and to anticipate the extension of the area of radio antennas.To this end, the researcher started by creating a Data warehouse that allows storing Big Data received from the Radio antennas. Then, doing the OLAP(online analytic processing) in order to perform multidimensional Analysis which used through GIS to represent the Data in different scales in satellite image as a topographic background). As a result, this prototype enables the carriers to receive continuous reports on different scales (Town, city, country) and to identify the BTS that works and performs well or shows the rate of its working (the way behaves) its pitfalls. By the end, it gives a clear image on the future working strategy respecting the urban planning, and the digital terrain model (DTM).

A Survey of Big Data Analytics Systems

Big Data ◽  
2016 ◽  
pp. 1024-1052
Author(s):  
M. Baby Nirmala
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Processing System ◽  
Full Potential ◽  
Analytical Processing ◽  
Tools And Techniques ◽  
New Generation ◽  
Analytical Platforms

In this emerging era of analytics 3.0, where big data is the heart of talk in all sectors, achieving and extracting the full potential from this vast data is accomplished by many vendors through their new generation analytical processing systems. This chapter deals with a brief introduction of the categories of analytical processing system, followed by some prominent analytical platforms, appliances, frameworks, engines, fabrics, solutions, tools, and products of the big data vendors. Finally, it deals with big data analytics in the network, its security, WAN optimization tools, and techniques for cloud-based big data analytics.

An Automatic Data Warehouse Conceptual Design Approach

2011 ◽  
pp. 110-119 ◽  
Author(s):  
Jamel Feki
Keyword(s):  
Information Acquisition ◽  
Large Data ◽  
Data Interpretation ◽  
Heterogeneous Data ◽  
Materialized Views ◽  
Data Warehouses ◽  
Automatic Data ◽  
Schema Design ◽  
On Line ◽  
Analytical Processing

Within today’s competitive economic context, information acquisition, analysis and exploitation became strategic and unavoidable requirements for every enterprise. Moreover, in order to guarantee their persistence and growth, enterprises are forced, henceforth, to capitalize expertise in this domain. Data warehouses (DW) emerged as a potential solution answering the needs of storage and analysis of large data volumes. In fact, a DW is a database system specialized in the storage of data used for decisional ends. This type of systems was proposed to overcome the incapacities of OLTP (On-Line Transaction Processing) systems in offering analysis functionalities. It offers integrated, consolidated and temporal data to perform decisional analyses. However, the different objectives and functionalities between OLTP and DW systems created a need for a development method appropriate for DW. Indeed, data warehouses still deploy considerable efforts and interests of a large community of both software editors of decision support systems (DSS) and researchers (Kimball, 1996; Inmon, 2002). Current software tools for DW focus on meeting end-user needs. OLAP (On-Line Analytical Processing) tools are dedicated to multidimensional analyses and graphical visualization of results (e.g., Oracle Discoverer?); some products permit the description of DW and Data Mart (DM) schemes (e.g., Oracle Warehouse Builder?). One major limit of these tools is that the schemes must be built beforehand and, in most cases, manually. However, such a task can be tedious, error-prone and time-consuming, especially with heterogeneous data sources. On the other hand, the majority of research efforts focuses on particular aspects in DW development, cf., multidimensional modeling, physical design (materialized views (Moody & Kortnik, 2000), index selection (Golfarelli, Rizzi, & Saltarelli 2002), schema partitioning (Bellatreche & Boukhalfa, 2005)) and more recently applying data mining for a better data interpretation (Mikolaj, 2006; Zubcoff, Pardillo & Trujillo, 2007). While these practical issues determine the performance of a DW, other just as important, conceptual issues (e.g., requirements specification and DW schema design) still require further investigations. In fact, few propositions were put forward to assist in and/or to automate the design process of DW, cf., (Bonifati, Cattaneo, Ceri, Fuggetta & Paraboschi, 2001; Hahn, Sapia & Blaschka, 2000; Phipps & Davis 2002; Peralta, Marotta & Ruggia, 2003).

A Multidimensional Model of Data Warehouses

2014 ◽  
Vol 989-994 ◽  
pp. 1657-1659
Author(s):  
Zu Yi Chen ◽  
Tai Xiang Zhao
Keyword(s):  
Lower Cost ◽  
Cost Estimate ◽  
Probability Analysis ◽  
Cost Data ◽  
Materialized Views ◽  
Multidimensional Model ◽  
Construction Costs ◽  
Historical Cost ◽  
On Line ◽  
Analytical Processing

Reducing query time by means of selecting a proper set of materialized views with a lower cost is crucial for effcient datawarehousing. The database, however, needs to be utilized more, by providing a functional environment of probability analysis. The objective of this paper is to improve the effectiveness of utilizing historical cost data in an analytical OLAP (On-Line Analytical Processing) environment. The results show that the OLAP environment can help understand the uncertainties inconstruction cost estimate, and provide a way for projecting more reliable construction costs.

Personalized Online Analytical Processing in Big Data Context Using User Profile and Search Context

2017 ◽  
Vol 8 (4) ◽  
pp. 67-80 ◽  
Author(s):  
Menaceur Sadek ◽  
Makhlouf Derdour ◽  
Bouramoul Abdelkrim
Keyword(s):  
Big Data ◽  
Large Data ◽  
User Profile ◽  
Relevant Information ◽  
Multidimensional Analysis ◽  
Online Analytical Processing ◽  
Data Sets ◽  
Process Data ◽  
Analytical Processing ◽  
Data Context

This article is part of the field of analysis and personalization of large data sets (Big Data). This aspect of analysis and customization has become a major issue that has generated a lot of questions in recent years. Indeed, it is difficult for inexperienced or casual users to extract relevant information in a Big Data context, for volume, the velocity and the variability of data make it difficult for the user to capture, manage and process data by methods and traditional tools. In this article, the authors propose a new approach for personalizing OLAP analysis in a Big Data context by using context and user profile. The proposed approach is based on five complementary layers namely: Extern layer, layer for the formulation of the contexts defined in the system, profiling and querying layer and layer for the construction of personalized OLAP cubes and a final one for multidimensional analysis cubes. The conducted experiment has shown that taking context and user profile into account improves the results of online analytical processing in the context of Big Data.

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