Supporting the ETL-process by Web Service technologies

Schlesinger, Lutz; Irmert, Florian; Lehner, Wolfgang

doi:10.1504/ijwgs.2005.007547

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…We will be able to take advantage of an enormous amount of data by using NoSQL databases [9], which will conform to the massive amount of unstructured data coming from social networks, with fast reading speed, better flow, desirable performance, dynamic schema, and the ability to upgrade the machine's memory and storage, etc.…”

Section: Collect Data (Nosql)mentioning

confidence: 99%

Business Intelligence Model to analyze Social Media through Big Data analytics

2021

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Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.

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Section: Collect Data (Nosql)mentioning

confidence: 99%

Business Intelligence Model to analyze Social Media through Big Data analytics

2021

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“…Web services are self-contained, self-describing modular applications that can be published, located, and invoked across the Web. Indeed, a service provides transparent access to a variety of relevant data sources as if they were a single source (Erl, 2004;Zhu et al, 2004;Schlesinger et al, 2005;Utomo, 2011). Moreover, according to informatica 1 , Software-as-a-Service (SaaS) integrate cloudbased data easily with on-premise systems to ensure users can access accurate, complete, up-to-date data whenever and wherever they need it.…”

Section: Axml-based Data Integration Frameworkmentioning

confidence: 99%

Active XML-based Web data integration

2013

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Today, the Web is the largest source of information worldwide. There is currently a strong trend for decision-making applications such as Data Warehousing (DW) and Business Intelligence (BI) to move onto the Web, especially in the cloud. Integrating data into DW/BI applications is a critical and time-consuming task. To make better decisions in DW/BI applications, next generation data integration poses new requirements to data integration systems, over those posed by traditional data integration. In this paper, we propose a generic, metadata-based, service-oriented, and event-driven approach for integrating Web data timely and autonomously. Beside handling data heterogeneity, distribution and interoperability, our approach satisfies near real-time requirements and realize active data integration. For this sake, we design and develop a framework that utilizes Web standards (e.g., XML and Web services) for tackling data heterogeneity, distribution and interoperability issues. Moreover, our framework utilizes Active XML (AXML) to warehouse passive data as well as services to integrate active and dynamic data on-the-fly. AXML embedded services and changes detection services ensure near real-time data integration. Furthermore, the idea of integrating Web data actively and autonomously revolves around mining events logged by the data integration environment. Therefore, we propose an incremental XML-based algorithm for mining association rules from logged events. Then, we define active rules dynamically upon mined data to automate and reactivate

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“…We also, in a first step, mainly focus on the definition of a query model that excludes update operations. Modelling the full ETL and warehouse refreshing processes is a complex task requiring processes that are out of the scope of this work (Schlesinger et al, 2005). Hence, we postpone this for now.…”

Section: Dweb Workloadmentioning

confidence: 99%

Benchmarking data warehouses

Darmont¹,

Bentayeb²,

Boussaïd³

2007

IJBIDM

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Abstract. Data warehouse architectural choices and optimization techniques are critical to decision support query performance. To facilitate these choices, the performance of the designed data warehouse must be assessed, usually with benchmarks. These tools can either help system users comparing the performances of different systems, or help system engineers testing the effect of various design choices. While the Transaction Processing Performance Council's standard benchmarks address the first point, they are not tunable enough to address the second one and fail to model different data warehouse schemas. By contrast, our Data Warehouse Engineering Benchmark (DWEB) allows generating various ad-hoc synthetic data warehouses and workloads. DWEB is implemented as a Java free software that can be interfaced with most existing relational database management systems. The full specifications of DWEB, as well as experiments we performed to illustrate how our benchmark may be used, are provided in this paper.

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Supporting the ETL-process by Web Service technologies

Cited by 12 publications

References 15 publications

Business Intelligence Model to analyze Social Media through Big Data analytics

Business Intelligence Model to analyze Social Media through Big Data analytics

Active XML-based Web data integration

Benchmarking data warehouses

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