Research

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Research Areas

The research focus of DMAL can be broadly classified into the following four areas:

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Advanced Data Management

The growth of the Internet in the last decade has dramatically changed the way in which information is managed and accessed. In this area we explore novel issues to manage such growing volumes of non-traditional data. In particular, we focus on several interesting issues such as efficient and scalable storage, indexing, and query processing strategies for complex data types such as tree and graph structured data, geo-spatial data, sequence and array data, data streams, multimedia data, Web and social data, mobile data, cloud data etc.


Data Analytics

Making sense of terabytes to petabytes of multi-source, complex-structured data demands advanced analytics and data science mechanism. In this arena, we focus on developing novel efficient and scalable algorithms for mining massive volumes of Web & social data, mobile data, biological and biomedical data, data streams, multimedia, graph-structured data, and XML data.


Information Retrieval

Search of information through various Internet-enabled devices plays an important role in our daily life. While the World Wide Web remains the main information repository for information search, the encouraged openness and information sharing through social platforms is reshaping the data distribution on the Web. As the result, the Web is abundant with user-created content. On the one hand, the user-created content is meta-data rich with temporal and spatial annotations and even the social connections among the data contributors. On the other hand, the user-created content is short, noisy, and lack of context. Such data reshaping on the Web leads to a renewed interests as well as new challenges in a broad range of IR problems. We research on innovative techniques to exploit the rich meta-data and to deal with the noisy nature of the user-created content for effective answering of various search queries.


Information Privacy & Security

Concepts like ubiquitous computing and ambient intelligence that exploit increasingly interconnected networks and mobility put new requirements on data management. An important element in the connected world is that data will be accessible anytime anywhere. This also has its downside in that it becomes easier to get unauthorized data access. Furthermore, it will become easier to collect, store, and search personal information and endanger peoplesĀ“ privacy. Therefore, secure and privacy-enhanced data management turns out to be a challenging goal that will also seriously influence the acceptance of ubiquitous computing and ambient intelligence concepts by society. In this research, we explore novel techniques in the arena of secured data access and processing, privacy preserving data management, privacy preserving data integration and mining.