ASD: the Alternative Splicing Database

Alternative splicing is widespread in mammalian gene expression, and variant splice patterns are often specific to different stages of development, particular tissues or a disease state. There is a need to systematically collect data on alternatively spliced exons, introns and splice isoforms, and to annotate this data. The Alternative Splicing Database consortium has been addressing this need, and is committed to maintaining and developing a value-added database of alternative splice events, and of experimentally verified regulatory mechanisms that mediate splice variants. In this paper we present two of the products from this project: namely, a database of computationally delineated alternative splice events as seen in alignments of EST/cDNA sequences with genome sequences, and a database of alternatively spliced exons collected from literature. The reported splice events are from nine different organisms and are annotated for various biological features including expression states and cross-species conservation. The data are presented on our ASD web pages (http://www.ebi.ac.uk/asd).

Multiresolution spatial databases: Making web-based spatial advances faster

Spatial data has now been used extensively in the Web environment, providing online customized maps and supporting map-based applications. The full potential of Web-based spatial applications, however, has yet to be achieved due to performance issues related to the large sizes and high complexity of spatial data. In this paper, we introduce a multiresolution approach to spatial data management and query processing such that the database server can choose spatial data at the right resolution level for different Web applications. One highly desirable property of the proposed approach is that the server-side processing cost and network traffic can be reduced when the level of resolution required by applications are low. Another advantage is that our approach pushes complex multiresolution structures and algorithms into the spatial database engine. That is, the developer of spatial Web applications needs not to be concerned with such complexity. This paper explains the basic idea, technical feasibility and applications of multiresolution spatial databases.

Searching the world wide web for local services and facilities: A review on the patterns of location-based queries

Many queries sent to search engines refer to specific locations in the world. Location-based queries try to find local services and facilities around the user’s environment or in a particular area. This paper reviews the specifications of geospatial queries and discusses the similarities and differences between location-based queries and other queries. We introduce nine patterns for location-based queries containing either a service name alone or a service name accompanied by a location name. Our survey indicates that at least 22% of the Web queries have a geospatial dimension and most of these can be considered as location-based queries. We propose that location-based queries should be treated different from general queries to produce more relevant results.

A web recommendation technique based on probabilistic latent semantic analysis

Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and identify the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.