HaoLap: A Hadoop based OLAP system for big data

Song, Jie; Guo, Chaopeng; Wang, Zhi; Zhang, Yichan; Yu, Ge; Pierson, Jean‐Marc

doi:10.1016/j.jss.2014.09.024

Cited by 47 publications

(19 citation statements)

References 25 publications

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“…In [9], a simplified multidimensional model is adopted to support OLAP operations over Hadoop, obtaining improved performance, regardless of the size of data sets and number of involved dimensions, on the basis of chunk partition and selection methods.…”

Section: Architecturementioning

confidence: 99%

“…As a counterpart, the methodology is quite complex because it integrates and reconciles both the requirement and the data oriented approaches [9]. The design methodology is based on two models that organize data both at the conceptual level, for representing multidimensional concepts in a graphical way, and at the logical level, for representing multidimensional schemas in a flexible way in reference to target NoSQL systems.…”

mentioning

confidence: 99%

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A Proposal of Methodology for Designing Big Data Warehouses

Tria¹,

Lefons²,

Tangorra³

2018

Preprint

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Big Data warehouses are a new class of databases that largely use unstructured and volatile data for analytical purpose. Examples of this kind of data sources are those coming from the Web, such as social networks and blogs, or from sensor networks, where huge amounts of data may be available only for short intervals of time. In order to manage massive data sources, a strategy must be adopted to define multidimensional schemas in presence of fast-changing situations or even undefined business requirements. In the paper, we propose a design methodology that adopts agile and automatic approaches, in order to reduce the time necessary to integrate new data sources and to include new business requirements on the fly. The data are immediately available for analyses, since the underlying architecture is based on a virtual data warehouse that does not require the importing phase. Examples of application of the methodology are presented along the paper in order to show the validity of this approach compared to a traditional one.

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Section: Architecturementioning

confidence: 99%

mentioning

confidence: 99%

A Proposal of Methodology for Designing Big Data Warehouses

Tria¹,

Lefons²,

Tangorra³

2018

Preprint

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“…Considering the past two decades have seen explosive growth, both the number of products and services offered and in the adoption of these technologies in industry, their other work [14] gave the introductions of OLAP and data warehouse technologies based on the new challenges of massively parallel data architecture [8].…”

Section: Related Workmentioning

confidence: 99%

“…Only storing the data in the datawarehouse is not profitable to the business until, the underlying data is analysed to gain business insights. Online Analytical Processing (OLAP) is an approach to answer multidimensional analytical queries swiftly, and provides support for decision-making and intuitive result views for queries [8].However, the traditional OLAP implementation, namely the ROLAP system based on RDBMS, appears to be inadequate in face of big data environment [8]. New massively parallel data architectures and analytic tools go beyond traditional parallel SQL datawarehouses and OLAP engines [8].…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

“…Online Analytical Processing (OLAP) is an approach to answer multidimensional analytical queries swiftly, and provides support for decision-making and intuitive result views for queries [8].However, the traditional OLAP implementation, namely the ROLAP system based on RDBMS, appears to be inadequate in face of big data environment [8]. New massively parallel data architectures and analytic tools go beyond traditional parallel SQL datawarehouses and OLAP engines [8]. Therefore, some databases such as SQL Server and MySQL are able to provide OLAP-like operations, but the performance cannot be satisfactory [14].…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

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Online Analytical Processing on Hadoop using Apache Kylin

Ranawade¹,

Navale²,

Dhamal³

et al. 2017

IJAIS

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In the Big Data age, it is necessary to remodel the traditional data warehousing and Online Analytical Processing (OLAP) system. Many challenges are posed to the traditional platforms due to the ever increasing data. In this paper, we have proposed a system which overcomes the challenges and is also beneficial to the business organizations. The proposed system mainly focuses on OLAP on Hadoop platform using Apache Kylin. Kylin is an OLAP engine which builds OLAP cubes from the data present in hive and stores the cubes in HBase for further analysis. The cubes stored in HBase are analyzed by firing SQL-like analytics queries. Also, reports and dashboards are further generated on the underlying cubes that provides powerful insights of the company data. This helps the business users to take decisions that are profitable to the organization. The proposed system is a boon to small as well as large scale business organizations. The aim of the paper is to present a system which builds OLAP cubes on Hadoop and generate insightful reports for business users.

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