504 research outputs found
Harnessing the power of the general public for crowdsourced business intelligence: a survey
International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI
Terms interrelationship query expansion to improve accuracy of Quran search
Quran retrieval system is becoming an instrument for users to search for needed
information. The search engine is one of the most popular search engines that
successfully implemented for searching relevant verses queries. However, a major
challenge to the Quran search engine is word ambiguities, specifically lexical
ambiguities. With the advent of query expansion techniques for Quran retrieval
systems, the performance of the Quran retrieval system has problem and issue in
terms of retrieving users needed information. The results of the current semantic
techniques still lack precision values without considering several semantic
dictionaries. Therefore, this study proposes a stemmed terms interrelationship query
expansion approach to improve Quran search results. More specifically, related terms
were collected from different semantic dictionaries and then utilize to get roots of
words using a stemming algorithm. To assess the performance of the stemmed terms
interrelationship query expansion, experiments were conducted using eight Quran
datasets from the Tanzil website. Overall, the results indicate that the stemmed terms
interrelationship query expansion is superior to unstemmed terms interrelationship
query expansion in Mean Average Precision with Yusuf Ali 68%, Sarawar 67%,
Arberry 72%, Malay 65%, Hausa 62%, Urdu 62%, Modern Arabic 60% and
Classical Arabic 59%
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