Researching Why-Not Questions in Skyline Query Based on Orthogonal Range

This paper aims to answer “why-not” questions in skyline queries based on the orthogonal query range (i.e., ORSQ). These queries retrieve skyline points within a rectangular query range, which improves query efficiency. Answering why-not questions in ORSQ can help users analyze query results and make decisions. We discuss the causes of why-not questions in ORSQ. Then, we outline how to modify the why-not point and the orthogonal query range so that the why-not point is included in the result of the skyline query based on the orthogonal range. When the why-not point is in the orthogonal range, we show how to modify the why-not point and narrow the orthogonal range. We also present how to expand the orthogonal range when the why-not point is not in the orthogonal range. We effectively combine query refinement and data modification techniques to produce meaningful answers. The experimental results demonstrate that the proposed algorithms have high-quality explanations for why-not questions in ORSQ in the real and synthetic datasets.

Personalization in Collaborative Fusion based Enterprise Information retrieval

Due to data spread across various heterogeneous data stores, information retrieval in Enterprise data stores is always challenging compared to web based retrieval systems. We have proposed a collaborative fusion based information retrieval in [1] using the observation on similar users tends to prefer similar search results. The solution applied three dimensions of user similarity, document similarity and user to documents affinity to a collaborative information fusion based retrieval. The work also proposed active feedback based search result revision to get highly relevant results. But the work did not have any provision for personalization and could not handle the cold start problems. Without consideration for cold start problems, the user to document affinity cannot be modeled accurately as the result, the collaborative fusion process is affected. In this work, we improve our earlier solution of collaborative fusion based information retrieval with consideration for user personalization and solution for cold start problems. The solution is based on query refinement using the information hidden in enterprise messaging systems. A user profile is built as vector of concepts using the information in enterprise messaging systems and this user profile concept vector is used to refine the query in way to personalize the results and avoid cold start problems. Compared to approach in [1], the proposed query refinement based personalization is able to increase the relevancy accuracy by 10% as obtained from experimental results.