Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

Hierarchical video summarisation in reference frame subspace

In this paper, a hierarchical video structure summarization approach using Laplacian Eigenmap is proposed, where a small set of reference frames is selected from the video sequence to form a reference subspace to measure the dissimilarity between two arbitrary frames. In the proposed summarization scheme, the shot-level key frames are first detected from the continuity of inter-frame dissimilarity, and the sub-shot level and scene level representative frames are then summarized by using k-mean clustering. The experiment is carried on both test videos and movies, and the results show that in comparison with a similar approach using latent semantic analysis, the proposed approach using Laplacian Eigenmap can achieve a better recall rate in keyframe detection, and gives an efficient hierarchical summarization at sub shot, shot and scene levels subsequently

Sentiment Analysis Using Collaborated Opinion Mining

Opinion mining and Sentiment analysis have emerged as a field of study since the widespread of World Wide Web and internet. Opinion refers to extraction of those lines or phrase in the raw and huge data which express an opinion. Sentiment analysis on the other hand identifies the polarity of the opinion being extracted. In this paper we propose the sentiment analysis in collaboration with opinion extraction, summarization, and tracking the records of the students. The paper modifies the existing algorithm in order to obtain the collaborated opinion about the students. The resultant opinion is represented as very high, high, moderate, low and very low. The paper is based on a case study where teachers give their remarks about the students and by applying the proposed sentiment analysis algorithm the opinion is extracted and represented.Comment: 5 pages, 6 figure

Identifying Privacy Policy in Service Terms Using Natural Language Processing

Ever since technology (tech) companies realized that people\u27s usage data from their activities on mobile applications to the internet could be sold to advertisers for a profit, it began the Big Data era where tech companies collect as much data as possible from users. One of the benefits of this new era is the creation of new types of jobs such as data scientists, Big Data engineers, etc. However, this new era has also raised one of the hottest topics, which is data privacy. A myriad number of complaints have been raised on data privacy, such as how much access most mobile applications require to function correctly, from having access to a user\u27s contact list to media files. Furthermore, the level of tracking has reached new heights, from tracking mobile phone location, activities on search engines, to phone battery life percentage. However much data is collected, it is within the tech companies\u27 right to collect the data because they provide a privacy policy that informs the user on the type of data they collect, how they use that data, and how they share that data. In addition, we find that all privacy policies used in this research state that by using their mobile application, the user agrees to their terms and conditions. Most alarmingly, research done on privacy policies has found that only 9% of mobile app users read legal terms and conditions [2] because they are too long, which is a worryingly low number. Therefore, in this thesis, we present two summarization programs that take in privacy policy text as input and produce a shorter summarized version of the privacy policy. The results from the two summarization programs show that both implementations achieve an average of at least 50%, 90%, and 85% on the same sentence, clear sentence, and summary score grading metrics, respectively

VSCAN: An Enhanced Video Summarization using Density-based Spatial Clustering

In this paper, we present VSCAN, a novel approach for generating static video summaries. This approach is based on a modified DBSCAN clustering algorithm to summarize the video content utilizing both color and texture features of the video frames. The paper also introduces an enhanced evaluation method that depends on color and texture features. Video Summaries generated by VSCAN are compared with summaries generated by other approaches found in the literature and those created by users. Experimental results indicate that the video summaries generated by VSCAN have a higher quality than those generated by other approaches.Comment: arXiv admin note: substantial text overlap with arXiv:1401.3590 by other authors without attributio