11140869
Effective keyword search in relational databases
With the amount of available text data in relational databases growing rapidly, the need for ordinary users to search such information is dramatically increasing. Even though the major RDBMSs have provided full-text search capabilities, they still require users to have knowledge of the database schemas and use a structured query language to search information. This search model is complicated for most ordinary users. Inspired by the big success of information retrieval (IR) style keyword search on the web, keyword search in relational databases has recently emerged as a new research topic. The differences between text databases and relational databases result in three new challenges: (1) Answers needed by users are not limited to individual tuples, but results assembled from joining tuples from multiple tables are used to form answers in the form of tuple trees. (2) A single score for each answer (i.e. a tuple tree) is needed to estimate its relevance to a given query. These scores are used to rank the most relevant answers as high as possible. (3) Relational databases have much richer structures than text databases. Existing IR strategies to rank relational outputs are not adequate. In this paper, we propose a novel IR ranking strategy for effective keyword search. We are the first that conducts comprehensive experiments on search effectiveness using a real world database and a set of keyword queries collected by a major search company. Experimental results show that our strategy is significantly better than existing strategies. Our approach can be used both at the application level and be incorporated into a RDBMS to support keyword-based search in relational databases.
7227804
Personalized web search by mapping user queries to categories
Current web search engines are built to serve all users, independent of the needs of any individual user. Personalization of web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to map a user query to a set of categories, which represent the user's search intention. This set of categories can serve as a context to disambiguate the words in the user's query. A user profile and a general profile are learned from the user's search history and a category hierarchy respectively. These two profiles are combined to map a user query into a set of categories. Several learning and combining algorithms are evaluated and found to be effective. Among the algorithms to learn a user profile, we choose the Rocchio-based method for its simplicity, efficiency and its ability to be adaptive. Experimental results indicate that our technique to personalize web search is both effective and efficient.
2900457
Database selection in intranet mediators for natural language queries
27655175
A Convex-Hull Based Algorithm to Connect the Maximal Independent Set in Unit-Disk Graphs
In this paper we propose and analyze a localized convex-hull based algorithm to connect a maximal independent set. The cardinality of the resultant connected dominating set is at most 76 · opt +19, where opt is the size of a minimum connected dominating set. To our knowledge, this is a dramatic improvement compared to the best published results in the same context [1,6] . Our algorithm plays an important rule in efficiently constructing a virtual backbone for ad hoc and sensor networks.
285048
Location-centric storage for safety warning based on roadway sensor networks
We propose a novel vision for roadway safety warning based on sensor networks, aiming at providing user-friendly zero-delay safety warnings to motorists. Our idea leverages the advanced sensing, networking and storage technologies. Roadway sensors detect events and store event records at multiple designated locations along the against traffic direction, such that the passing-by drivers can be alerted to potential dangers or traffic delays through the wireless communication between roadway sensors and the vehicle. We design a location-centric storage (LCS) protocol, which manages the propagation and storage of event records based on the time needed to clear the road. In LCS, the density of the sensors storing an event record decreases logarithmically with respect to the distance to the event location. Thus, the closer to the event position, the more number of warnings a driver may obtain. LCS is further tailored for the case of ''highway'' sensor networks when all sensors are deployed along a straight line mimicking a highway, and the more complex case when two roads intersect at some place. We conduct both theoretic analysis and simulation study to verify the performance of LCS when applied to roadway sensor networks for safety warning. The results indicate that LCS is fair to all sensors. We conclude that roadway safety warning based on sensor networks is a promising idea for realizing ITS's ''Zero Fatality, Zero Delay'' roadway safety philosophy.
18981738
Dynamic Range Query in Spatial Network Environments
Moving range queries over mobile objects are important in many location management applications. There have been quite a few research works in this area. However, all existing solutions assume an open space environment, which are either not applicable to spatial network environment or require non-trivial extensions. In this paper, we consider a new class of query called Dynamic Range Query. A dynamic range query is a moving range query in a network environment, which retrieves the moving objects within a specified network distance of the moving query point. As this query point moves in the network, the footprint (or shape) of the query range changes accordingly to reflect the new relevant query area. Our execution strategy leverages computing power of the moving objects to reduce server load and communication costs. This scheme is particularly desirable for many practical applications such as vehicles in a street environment, where mobile energy is not an issue. We describe the design details and present our simulation study. The performance results indicate that our solution is almost two magnitudes better than a query index method in terms of server load, and requires similar number of messages when compared to a query-blind optimal scheme.
17825087
Feature Matrix Models for Information Extraction
To extract useful information which really contribute to the target issue from huge amounts of data or text description is an important task towards a number of research fields (e.g., genomics study and text mining). In this paper, a general feature matrix model (FMM) is proposed aiming to provide partial answer to this task. Specifically, one instantiation of FMM is used for identifying feature genes based on microarray data, and the other for extracting information according to case description.
8017889
An architecture of wireless Web and dialogue system convergence for multimodal service interaction over converged networks
This paper presents the software architecture for a new paradigm of wireless Web and dialogue system convergence and its corresponding approach to deliver multimodal multimedia enterprise communication services in the wireless environment. It uses an event-triggered service delivery method that separates service interaction and wireless web-based service delivery. A customized content presentation approach is adopted to transform the content of the service into a format suitable for presentation on the target devices. The system also contains a service delivery monitoring and backup mechanism, which is capable of sending the service content using other communication channels according to the user's preference and the channel conditions. A prototype system was implemented based on the proposed approach, which demonstrates the performance advantage of our method in the enterprise service application.