Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources, the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services, and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review

© 2013 IEEE. The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented

A personal route prediction system based on trajectory data mining

This paper presents a system where the personal route of a user is predicted using a probabilistic model built from the historical trajectory data. Route patterns are extracted from personal trajectory data using a novel mining algorithm, Continuous Route Pattern Mining (CRPM), which can tolerate different kinds of disturbance in trajectory data. Furthermore, a client–server architecture is employed which has the dual purpose of guaranteeing the privacy of personal data and greatly reducing the computational load on mobile devices. An evaluation using a corpus of trajectory data from 17 people demonstrates that CRPM can extract longer route patterns than current methods. Moreover, the average correct rate of one step prediction of our system is greater than 71%, and the average Levenshtein distance of continuous route prediction of our system is about 30% shorter than that of the Markov model based method

Pattern Mining and Sense-Making Support for Enhancing the User Experience

While data mining techniques such as frequent itemset and sequence mining are well established as powerful pattern discovery tools in domains from science, medicine to business, a detriment is the lack of support for interactive exploration of high numbers of patterns generated with diverse parameter settings and the relationships among the mined patterns. To enhance the user experience, real-time query turnaround times and improved support for interactive mining are desired. There is also an increasing interest in applying data mining solutions for mobile data. Patterns mined over mobile data may enable context-aware applications ranging from automating frequently repeated tasks to providing personalized recommendations. Overall, this dissertation addresses three problems that limit the utility of data mining, namely, (a.) lack of interactive exploration tools for mined patterns, (b.) insufficient support for mining localized patterns, and (c.) high computational mining requirements prohibiting mining of patterns on smaller compute units such as a smartphone. This dissertation develops interactive frameworks for the guided exploration of mined patterns and their relationships. Contributions include the PARAS pre- processing and indexing framework; enabling analysts to gain key insights into rule relationships in a parameter space view due to the compact storage of rules that enables query-time reconstruction of complete rulesets. Contributions also include the visual rule exploration framework FIRE that presents an interactive dual view of the parameter space and the rule space, that together enable enhanced sense-making of rule relationships. This dissertation also supports the online mining of localized association rules computed on data subsets by selectively deploying alternative execution strategies that leverage multidimensional itemset-based data partitioning index. Finally, we designed OLAPH, an on-device context-aware service that learns phone usage patterns over mobile context data such as app usage, location, call and SMS logs to provide device intelligence. Concepts introduced for modeling mobile data as sequences include compressing context logs to intervaled context events, adding generalized time features, and identifying meaningful sequences via filter expressions