scholarly journals Enterprise Business Intelligence Data Preparation Using RDF Data Sources

2007 ◽  
pp. 443-453
Author(s):  
Wajee Teswanich ◽  
Suphamit Chittayasothorn
Keyword(s):  
Business Intelligence ◽  
Data Sources ◽  
Data Preparation ◽  
Rdf Data

scholarly journals DATA SOURCES FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Personal Data ◽  
Data Sources ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
External Data ◽  
Operational Systems ◽  
Internal Sources ◽  
Key Questions

As organizations' desire for data grows, so does their search for data sources that are both usable and reliable.Businesses can obtain and collect big data in a variety of locations, both inside and outside their own walls.This study aims to investigate the various data sources for business intelligence. For business intelligence,there are three types of data: internal data, external data, and personal data. Internal data is mostly kept indatabases, which serve as the backbone of an enterprise information system and are known as transactionalsystems or operational systems. This information, however, is not always sufficient. If the company wants toanswer market and industry questions or better understand future customers, the analytics team may need to look beyond the company's own data sources. Organizations must have access to a variety of data sources in order to answer the key questions that guide their initiatives. Internal sources, external public sources, andcollaboration with a big data expert could all be beneficial. Companies who are able to extract relevant datafrom their mountain of data acquire new perspectives on their business, allowing them to become morecompetitive

scholarly journals Business Intelligence Infrastructure for Academic Libraries

2013 ◽  
Vol 8 (2) ◽  
pp. 172 ◽  
Author(s):  
Joe Zucca
Keyword(s):  
Business Intelligence ◽  
Resource Sharing ◽  
User Behavior ◽  
Quantitative Information ◽  
Data Sources ◽  
Activity Data ◽  
Extensible Markup ◽  
Binary Object ◽  
The University ◽  
Transactional Data

Objective – To describe the rationale for and development of MetriDoc, an information technology infrastructure that facilitates the collection, transport, and use of library activity data. Methods – With the help of the Institute for Museum and Library Services, the University of Pennsylvania Libraries have been working on creating a decision support system for library activity data. MetriDoc is a means of “lighting up” an array of data sources to build a comprehensive repository of quantitative information about services and user behavior. A data source can be a database, text file, Extensible Markup Language (XML), or any binary object that contains data and has business value. MetriDoc provides simple tools to extract useful information from various data sources; transform, resolve, and consolidate that data; and finally store them in a repository. Results – The Penn Libraries completed five reference projects to prove basic concepts of the MetriDoc framework and make available a set of applications that other institutions could test in a deployment of the MetriDoc core. These reference projects are written as configurable plugins to the core framework and can be used to parse and store EZ-Proxy log data, COUNTER data, interlibrary loan transactional data from ILLIAD, fund expenditure data from the Voyager integrated library system, and transactional data from the Relais platform, which supports the BorrowDirect and EZBorrow resource sharing consortiums. The MetriDoc framework is currently undergoing test implementations at the University of Chicago and North Carolina State University, and the Kuali-OLE project is actively considering it as the basis of an analytics module. Conclusion – If libraries decide that a business intelligence infrastructure is strategically important, deep collaboration will be essential to progress, given the costs and complexity of the challenge.

Completeness Management for RDF Data Sources

2018 ◽  
Vol 12 (3) ◽  
pp. 1-53 ◽  
Author(s):  
Fariz Darari ◽  
Werner Nutt ◽  
Giuseppe Pirrò ◽  
Simon Razniewski
Keyword(s):  
Data Sources ◽  
Rdf Data

scholarly journals STRATEGI MENGKONSTRUKSI KOMPETITIF BISNIS MELALUI MODAL INTELEKTUAL DAN KECERDASAN PRODUKSI

2015 ◽  
Vol 12 (1) ◽  
Author(s):  
Ismail Nawawi
Keyword(s):  
Intellectual Capital ◽  
Business Intelligence ◽  
Data Reduction ◽  
Business Strategy ◽  
Business Development ◽  
Data Sources ◽  
Data Display ◽  
Key Informant ◽  
Marketing Data ◽  
Intelligence Approach

The study was conducted to determine the competitive strategy of constructing bsnis throughintellectual capital and intelligence production. The results are used as recommendations on thecompany's business development. The research was conducted with qualitative methodsapproach to the type of case studies. Data collection techniques with participation observation,interviews and documentation studies. Samples and data sources specified in perposive andsnowbal sampling, key informant Director, Director of Investment and Business Development,and Director of Corporate Marketing. Data analysis using analysis of thesis, antithesis andsentesa and the process is done with the data reduction, data display and verivication. The resultsof this study indicates: (a) capable of doing the business strategy-based approach to intellectualcapital, (b) capable of implementing a business intelligence approach to production. Penelelitiangoal is to (a) understand and describe the business strategy-based approach to intellectual capital,(b) understand and describe the implementation of business intelligence approach to theproduction. Based on these results, the model can be found menkonstruksi competitive businessstrategy to prepare the implementation of business intelligence approach to production inaccordance with the demands, needs and tastes of consumers.Penelitian ini dilakukan untuk mengetahui strategi mengkonstruksi kompetitif bsnis melaluimodal intelektual dan kecerdasan produksi. Hasilnya digunakan sebagai bahan rekomendasipengembangan bisnis di perusahaan tersebut. Penelitian ini dilakukan dengan pendekatan metodakualitatif dengan jenis studi kasus. Teknik pengumpulan data dengan observasi peranserta,wawancara mendalam dan studi dokumentasi. Sampel dan sumber data ditentukan secaraperposive dan snowbal sampling, dengan informan kunci Direktur Utama, Direktur Investasi danPengembangan Usaha, dan Direktur Pemasaran. Analisis data menggunakan analisis tesa,antitesa dan sentesa dan prosesnya dilakukan dengan data reduction, data display danverification. Hasil penelitian ini menunjukan: (a) mampu melakukan strategi bisnis berbasispendekatan modal intelektual, (b) mampu melakukan implementasi bisnis dengan pendekatankecerdasan produksi. Tujuan penelelitian ini untuk (a) memahami dan mendiskripsikan strategibisnis berbasis pendekatan modal intelektual, (b) memahami dan mendiskripsikan implementasibisnis dengan pendekatan kecerdasan produksi. Berdasarkan hasil penelitian tersebut, dapatditemukan model strategi menkonstruksi kompetitif bisnis untuk menyiapkan implementasibisnis dengan pendekatan kecerdasan produksi sesuai dengan tuntutan, kebutuhan dan selerakonsumen.

scholarly journals A Scalable Business Intelligence Decision-Making System in the Era of Big Data

2019 ◽  
Vol 8 (12) ◽  
pp. 2034-2042
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Data Storage ◽  
Business Intelligence ◽  
Unstructured Data ◽  
Data Sources ◽  
Second Step ◽  
Decision Making System ◽  
Set Up

Transformation presents the second step in the ETL process that is responsible for extracting, transforming and loading data into a data warehouse. The role of transformation is to set up several operations to clean, to format and to unify types and data coming from multiple and different data sources. The goal is to get data to conform to the schema of the data warehouse to avoid any ambiguity problems during the data storage and analytical operations. Transforming data coming from structured, semi-structured and unstructured data sources need two levels of treatments: the first one is transformation schema to schema to get a unified schema for all selected data sources and the second treatment is transformation data to data to unify all types and data gathered. To ensure the setting up of these steps we propose in this paper a process switch from one database schema to another as a part of transformation schema to schema, and a meta-model based on MDA approach to describe the main operations of transformation data to data. The results of our transformations propose a data loading in one of the four schemas of NoSQL to best meet the constraints and requirements of Big Data.

An Autocompletion Mechanism for Enriched Keyword Queries to RDF Data Sources

2013 ◽  
pp. 601-612
Author(s):  
Grégory Smits ◽  
Olivier Pivert ◽  
Hélène Jaudoin ◽  
François Paulus
Keyword(s):  
Data Sources ◽  
Rdf Data
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