Data Mining - Concepts and Techniques.

Hawaii International Conference on System Sciences

Twitter has been used as one of the communication channels for spreading breaking news. We propose a method to collect, group, rank and track breaking news in Twitter. Since short length messages make similarity comparison difficult, we boost scores on proper nouns to improve the grouping results. Each group is ranked based on popularity and reliability factors. Current detection method is limited to facts part of messages. We developed an application called “Hotstream” based on the proposed method. Users can discover breaking news from the Twitter timeline. Each story is provided with the information of message originator, story development and activity chart. This provides a convenient way for people to follow breaking news and stay informed with real-time updates.

Breaking News Detection and Tracking in Twitter.

Management of the enterprise architecture has become increasingly recognized as a crucial part of both business and IT management. Still, a common understanding and methodological consistency seems far from being developed. Acknowledging the significant role of research in moving the development process along, this article employs different bibliometric methods, complemented by an extensive qualitative interpretation of the research field, to provide a unique overview of the enterprise architecture literature. After answering our research questions about the collaboration via co-authorships, the intellectual structure of the research field and its most influential works, and the principal themes of research, we propose an agenda for future research based on the findings from the above analyses and their comparison to empirical insights from the literature. In particular, our study finds a considerable degree of co-authorship clustering and a positive impact of the extent of co-authorship on the diffusion of works on enterprise architecture. In addition, this article identifies three major research streams and shows that research to date has revolved around specific themes, while some of high practical relevance receive minor attention. Hence, the contribution of our study is manifold and offers support for researchers and practitioners alike.

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An Exploration of Enterprise Architecture Research

Daily PM 2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm

Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting

Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naive Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naive Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.

/pdf/stock-market-classification-model-using-sentiment-analysis-4vhdh787ru.pdf

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Content-based image retrieval is an active research issue that had been famous from 1990s till presentThe main target of CBIR is to get accurate results with lower computational timeThis research report discusses on the comparative method used in color histogram based on three major methods used frequently in CBIR which are; normal color histogram using GLCM, color histogram using K-Means, and color histogram using Gabor filteringA set of 1000 images that cover 6 categories of images from bus, beach, building, flower, horse, and mountain are used to test the accuracy and the precision of each methodUsing Euclidean distance, similarity between queried image and the candidate images are calculatedExperiment results shows that color histogram with GLCM method had high accuracy and precise compared to K-Means and GaborFuture work will be made to add more features that are famous in CBIR which are texture, color, and shape features in order to get better results

Comparative analysis of content-based image retrieval techniques

In this paper we described the findings based on a research study on current enterprise information architecture (EIA) practices in Malaysian organizations. Ten organizations from public and private sectors were chosen for case study analysis. The Zachman framework was chosen as a guideline to assess the current practice of EIA in these organizations. This study had successfully investigated the current practice and conditions of EIA in selected public and private organizations in Malaysia. The study found that majority of the organizations do practice some kind of enterprise information architecture either in-house or outsource to third parties. The study also found that certain aspects of the framework were not addressed at all, whilst other aspects that were addressed vary in terms of the different perspectives. This gives a general outlook of EIA implementation in the selected organizations, which could be incomplete or not adequately addressed. The study revealed a poor knowledge and understanding of EIA among the organizations though there had been efforts at implementing EIA focusing on the data, function and network architectures. The study discovered gaps in the current practice and provides suggestions for organizations to consciously embark on the EIA paradigm in order to better align the whole organization to its goals. Results of this study can be used by the government and private sectors to formulate new policies and guidelines on enterprise architecture so that the enterprise's IT adoption and information requirements fit nicely into its business strategy

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/12OmNyQGS7q/pdf

Enterprise Information Architecture (EIA): Assessment of Current Practices in Malaysian Organizations

Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

Siti Sakira Kamaruddin

Papers

Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Comparative analysis of content-based image retrieval techniques

Enterprise Information Architecture (EIA): Assessment of Current Practices in Malaysian Organizations

A Survey on Event Detection Models for Text Data Streams