Privacy Aware Parallel Computation of Skyline Sets Queries from Distributed Databases

A skyline query finds objects that are not dominated by another object from a given set of objects. Skyline queries help us to filter unnecessary information efficiently and provide us clues for various decision making tasks. However, we cannot use skyline queries in privacy aware environment, since we have to hide individual's records values even though there is no ID information. Therefore, we considered skyline sets queries. The skyline set query returns skyline sets from all possible sets, each of which is composed of some objects in a database. With the growth of network infrastructure data are stored in distributed databases. In this paper, we expand the idea to compute skyline sets queries in parallel fashion from distributed databases without disclosing individual records to others. The proposed method utilizes an agent-based parallel computing framework that can efficiently compute skyline sets queries and can solve the privacy problems of skyline queries in distributed environment. The computation of skyline sets is performed simultaneously in all databases which increases parallelism and reduces the computation time

スカイライン問合わせを利用した大規模データベースの情報選別

Conventional SQL queries take exact input and produce complete result set. However, with massive increase in data volume in different applications, the large result sets returned by traditional SQL queries are not well suited for the users to take effective decisions. Therefore, there is an increasing interest in queries like top-k queries and skyline queries those produce a more concise result set. Top-k queries rely on the scores of the objects to evaluate the usefulness of the objects. In this type of queries, users require to define their own scoring function by combining their interests. Based on the user defined scoring function, the system sorts the objects by their scores and outputs the top-k objects in the ranking list as the result. However, defining a scoring function by the users is a major draw of the top-k queries as in the large data sets where there are many conflicting criteria exist, it is very difficult for the users to define the scoring functions by themselves.……広島大学(Hiroshima University)博士(工学)Engineeringdoctora

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Machinic Eyes: New and Post-Digital Aesthetics, Surveillance, and Resistance

This work concerns the rise of the New Aesthetic, an art project developed by James Bridle in 2012. The New Aesthetic, as envisioned by Bridle, was chiefly concerned with the overlapping of physical and digital realities through both the artifacts produced by this overlapping and the systems involved therein. I introduce the advent of the New Aesthetic and present the major criticisms: the lack of a robust theoretical and scholarly framework, the lack of a historical framework, the privileging of artifacts over systems as new Aesthetic, and the fragmented scholarly outlook on the New Aesthetic. Upon further examination, I discovered that the New Aesthetic is less of an art project but a metaphor for a global surveillance apparatus that is the result of clandestine partnerships between multinational technology corporations and intelligence agencies associated the Five Eyes consortium. In this dissertation, I critique the New Aesthetic from a scholarly viewpoint, offer a historical precedent of how the New Aesthetic came to be from cultural and technological perspectives, examine the rise of the global surveillance apparatus within the New Aesthetic, and offer ideas of how to resist surveillance as a result of our reliance upon computational technologies

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining