2096552
Keyword search on relational data streams
Increasing monitoring of transactions, environmental parameters, homeland security, RFID chips and interactions of online users rapidly establishes new data sources and application scenarios. In this paper, we propose keyword search on relational data streams (S-KWS) as an effective way for querying in such intricate and dynamic environments. Compared to conventional query methods, S-KWS has several benefits. First, it allows search for combinations of interesting terms without a-priori knowledge of the data streams in which they appear. Second, it hides the schema from the user and allows it to change, without the need for query re-writing. Finally, keyword queries are easy to express.
 Our contributions are summarized as follows. (i) We provide formal semantics for S-KWS, addressing the temporal validity and order of results. (ii) We propose an efficient algorithm for generating operator trees, applicable to arbitrary schemas. (iii) We integrate these trees into an operator mesh that shares common expressions. (iv) We develop techniques that utilize the operator mesh for efficient query processing. The techniques adapt dynamically to changes in the schema and input characteristics. Finally, (v) we present methods for purging expired tuples, minimizing either CPU, or memory requirements.
5522263
HybMig: A Hybrid Approach to Dynamic Plan Migration for Continuous Queries
In data stream environments, the initial plan of a long-running query may gradually become inefficient due to changes of the data characteristics. In this case, the query optimizer generates a more efficient plan based on the current statistics. The online transition from the old to the new plan is called dynamic plan migration. In addition to correctness, an effective technique for dynamic plan migration should achieve the following objectives: 1) minimize the memory and CPU overhead of the migration, 2) reduce the duration of the transition, and 3) maintain a steady output rate. The only known solutions for this problem are the moving states (MS) and parallel track (PT) strategies, which have some serious shortcomings related to the above objectives. Motivated by these shortcomings, we first propose HybMig, which combines the merits of MS and PT and outperforms both in every aspect. As a second step, we extend PT, MS, and HybMig to the general problem of migration, where both the new and the old plans are treated as black boxes
150914
An enhanced model for searching in semantic portals
Semantic Portal is the next generation of web portals that are powered by Semantic Web technologies for improved information sharing and exchange for a community of users. Current methods of searching in Semantic Portals are limited to keyword-based search using information retrieval (IR) techniques, ontology-based formal query and reasoning, or a simple combination of the two. In this paper, we propose an enhanced model that tightly integrates IR with formal query and reasoning to fully utilize both textual and semantic information for searching in Semantic Portals. The model extends the search capabilities of existing methods and can answer more complex search requests. The ideas in a fuzzy description logic (DL) IR model and a formal DL query method are employed and combined in our model. Based on the model, a semantic search service is implemented and evaluated. The evaluation shows very large improvements over existing methods.
5091874
Integrating Web Services into Ontology-Based Web Portal
With the explosive emerged Web services, the Web becomes a world of information and applications. So portals whose goal is presenting a structured view onto the Web should provide users with a single point access to multiple types of not only information but also services. Recently, many systems have been developed using ontology-driven approaches to automatically generate and manage Web portals. However, all of them lack the integration of Web services. In this paper, we describe SPortS, an OWL-DL(Web Ontology Language-Description Logics) based portal generation system that integrates semantic Web services into generated portals at the presentation level, the data level and the function level. We present the portal generation process, the query decomposition and the service recommendation algorithms through which semantic Web services are integrated tightly into the generated portal.
6243197
An application of least squares fit mapping to text information retrieval
This paper describes a unique example-based mapping method for document retrieval. We discovered that the knowledge about relevance among queries and documents can be used to obtain empirical connections between query terms and the canonical concepts which are used for indexing the content of documents. These connections do not depend on whether there are shared terms among the queries and documents; therefore, they are especially effective for a mapping from queries to the documents where the concepts are relevant but the terms used by article authors happen to be different from the terms of database users. We employ a Linear Least Squares Fit (LLSF) technique to compute such connections from a collection of queries and documents where the relevance is assigned by humans, and then use these connections in the retrieval of documents where the relevance is unknown. We tested this method on both retrieval and indexing with a set of MEDLINE documents which has been used by other information retrieval systems for evaluations. The effectiveness of the LLSF mapping and the significant improvement over alternative approaches was evident in the tests.
11534248
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Prediction in streaming data is an important activity in the modern society. Two major challenges posed by data streams are (1) the data may grow without limit so that it is difficult to retain a long history of raw data; and (2) the underlying concept of the data may change over time. The novelties of this paper are in four folds. First, it uses a measure of conceptual equivalence to organize the data history into a history of concepts. This contrasts to the common practice that only keeps recent raw data. The concept history is compact while still retains essential information for learning. Second, it learns concept-transition patterns from the concept history and anticipates what the concept will be in the case of a concept change. It then proactively prepares a prediction model for the future change. This contrasts to the conventional methodology that passively waits until the change happens. Third, it incorporates proactive and reactive predictions. If the anticipation turns out to be correct, a proper prediction model can be launched instantly upon the concept change. If not, it promptly resorts to a reactive mode: adapting a prediction model to the new data. Finally, an efficient and effective system RePro is proposed to implement these new ideas. It carries out prediction at two levels, a general level of predicting each oncoming concept and a specific level of predicting each instance's class. Experiments are conducted to compare RePro with representative existing prediction methods on various benchmark data sets that represent diversified scenarios of concept change. Empirical evidence offers inspiring insights and demonstrates the proposed methodology is an advisable solution to prediction in data streams.
17661227
RDF-based Knowledge Models for Network Management
SMIng (next generation structure of management information), an information model for network management, is a prospective structure of management information. When deploying the multi-agent systems to network management environments, we have established a lightweight self-contained knowledge model based on RDF (resource description framework) and its extensions. We also present an implementation prototype to support agent communication and coordination by RDF-based languages.
1977839
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Recently, mining from data streams has become an important and challenging task for many real-world applications such as credit card fraud protection and sensor networking. One popular solution is to separate stream data into chunks, learn a base classifier from each chunk, and then integrate all base classifiers for effective classification. In this paper, we propose a new dynamic classifier selection (DCS) mechanism to integrate base classifiers for effective mining from data streams. The proposed algorithm dynamically selects a single “best” classifier to classify each test instance at run time. Our scheme uses statistical information from attribute values, and uses each attribute to partition the evaluation set into disjoint subsets, followed by a procedure that evaluates the classification accuracy of each base classifier on these subsets. Given a test instance, its attribute values determine the subsets that the similar instances in the evaluation set have constructed, and the classifier with the highest classification accuracy on those subsets is selected to classify the test instance. Experimental results and comparative studies demonstrate the efficiency and efficacy of our method. Such a DCS scheme appears to be promising in mining data streams with dramatic concept drifting or with a significant amount of noise, where the base classifiers are likely conflictive or have low confidence.
12902641
A compact improved quadtree representation with image manipulations
Abstract In this paper, a new and compact improved quadtree (compact-IQ) representation is presented for representing binary images. Then, efficient set operations on compact-IQs are also presented. Some experimentations are carried out to demonstrate the memory-efficiency and computational advantages of the proposed method. The experimental results reveal that the proposed image representation has 11.04–18.80% compression improvement and 24.39–36.94% computation-time improvement for set operations when compared to the recently published method on the constant bit-length linear quadtrees (CBLQ) (T.W. Lin, Set operations on constant bit-length linear quadtrees, Pattern Recognition 30(7) (1997) 1239–1249). In addition, geometric operations (area and centroid) on the compact-IQ and the performance comparison with JBIG are also investigated.