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In this paper, we propose an approach for surveillance video indexing and retrieval. The objective of this approach is to answer five main challenges we have met in this domain: (1) the lack of means for finding data from the indexed databases, (2) the lack of approaches working at different abstraction levels, (3) imprecise indexing, (4) incomplete indexing, (5) the lack of user-centered search. We propose a new data model containing two main types of extracted video contents: physical objects and events. Based on this data model, we present a new rich and flexible query language. This language works at different abstraction levels, provides both exact and approximate matching and takes into account users' interest. In order to work with the imprecise indexing, two new methods respectively for object representation and object matching are proposed. Videos from two projects which have been partially indexed are used to validate the proposed approach. We have analyzed both query language usage and retrieval results. The obtained retrieval results analyzed by the average normalized ranks are promising. The retrieval results at the object level are compared with another state of the art approach.

Structured Query Language (SQL) is the dominant language for managing relational databases. However, complex SQL queries are hard to write and maintain because of the intricate inter-table and inter-column relations. To this end, we propose a novel query language called SQL#, which allows programmers to construct complex queries module by module and explicitly specify the relations between different modules according to the logical steps of constructing queries. Besides, we design a SQL#-based system, aiming to facilitate the maintenance of SQL# queries. Specifically, the system renders a SQL# program into a hierarchical graph, which could help programmers understand the high-level structures of SQL# programs and the intricate relations between different components within SQL# programs. In addition, the system can ease the generation of the intermediate tables that correspond to the logical steps of constructing queries, which could help programmers debug complex SQL# queries. Notably, the design of SQL# makes it easy for the system to generate the hierarchical graph and the intermediate tables. Controlled experiments suggest that the SQL#-based system reduces the durations of writing and understanding database queries by 79% and 39%, respectively, compared to raw SQL code.

In order to support uniform access to heterogeneous temporal information, we introduce the concept of a temporal mediator. A temporal mediator consists of three components: (i) a repository for windowing functions and conversion functions, (ii) a time granularity thesaurus and (iii) a query interpreter. There are two types of windowing functions: one associates each time point to a set of tuples, and the other associates each tuple to a set of time points. A conversion function transforms information in terms of one time granularity into that in terms of another time granularity. The time granularity thesaurus stores the knowledge about time granularities (e.g., names of time granularities and relationships among them). Users pose queries using the windowing functions and in terms of desired time granularities. (A query language, which can be used to form such queries, is given in the paper.) To answer such a user query, the query interpreter first employs the windowing functions together with the time granularity thesaurus to retrieve needed temporal data from the underlying databases and then uses the time granularity thesaurus to select suitable conversion functions which convert the responses to the desired time granularities. Thus, a temporal mediator provides a simple interface that supports uniform accesses to heterogeneous temporal databases.

There has been a considerable amount of work on object-oriented databases, active databases, and deductive databases. The common objective of these efforts is to produce highly intelligent and active systems for supporting the next generation of database applications. These future systems must be capable of capturing the concepts of time and managing not just temporal data but temporal knowledge expressed by knowledge rules. In this paper, we describe our efforts on a temporal object-oriented knowledge model, OSAM*/T, its associated temporal query language, OQL/T, an underlying temporal algebra, TA-algebra, and some implementation techniques. In addition to the features of the traditional object-oriented paradigm, the model is characterized by its strong support of association types and its incorporation of temporal knowledge rules for specifying temporal and other types of semantic constraints associated with object classes and their temporal object instances. The query language is featured by its pattern-based specification of temporal object associations, which allows complex queries with various time constraints to be formulated in a relatively simple way. The temporal algebra provides a set of primitive operators for manipulating homogeneous and/or heterogeneous patterns of temporal object associations, thus providing the needed mathematical foundation for processing and optimizing temporal queries. The implementation techniques include a Delta-Instance and Multi-Snapshot Storage Model, as well as data partitioning and clustering schemes for storage management of temporal knowledge bases.

This paper describes a search engine for multimedia web documents and a methodology for removing (partially or totally) redundant information from multiple documents in an effort to synthesize new documents. In this paper, a typical multimedia document contains free text and images and additionally has associating well-structured data. An SQL-like query language, WebSSQL, is proposed to retrieve this type of documents. The main differences between WebSSQL and other proposed SQL extensions for retrieving web documents are that WebSSQL is similarity-based and supports conditions on images. This paper also deals with the detection and removal of redundant information (text paragraphs and images) from multiple retrieved documents. Documents reporting the same or related events and stories may contain substantial redundant information. The removal of the redundant information and the synthesis of these documents into a single document can not only save a user's time to acquire the information but also storage space to archive the data. The methodology reported here consists of techniques for analyzing text paragraphs and images as well as a set of similarity criteria used to detect redundant paragraphs and images. Examples are provided to illustrate these techniques.

The amount of online information in Chinese and the number of Chinese Internet users have been increasing tremendously during the past decade. Since Chinese language is significantly different from English, techniques that have been developed for retrieving information from English Web documents cannot be directly applied to retrieve information from Chinese Web documents. In order to provide high-performance access of Chinese information on the Web, we have developed a Chinese Web query engine that (i) extracts (hierarchical) data of interest from Chinese HTML tables using an information extraction tool called semantic hierarchy, (ii) allows the user to submit queries in Chinese using a menu-driven user interface, and (iii) processes the user's queries (as Boolean expressions) to generate the correct results. Our query engine supports various groups of information that are categorized into various subject areas, such as car ads, house rentals, job ads, stocks, university catalogs, etc. We have tested our information extraction tool on two application domains, car-ads and house-rental. The average F-measure on extracting Chinese data from these two application domains is above 90%. More importantly, our query engine can easily be configured and internationalized to become a worldwide, multilingual query engine with minor changes in system settings on PCs running Windows operating systems.

Metabolism is a representation of the biochemical principles that govern the production, consumption, degradation, and biosynthesis of metabolites in living cells. Organisms respond to changes in their physiological conditions or environmental perturbations (i.e. constraints) via cooperative implementation of such principles. Querying inner working principles of metabolism under different constraints provides invaluable insights for both researchers and educators. In this paper, we propose a metabolism query language (MQL) and discuss its query processing. MQL enables researchers to explore the behavior of the metabolism with a wide-range of predicates including dietary and physiological condition specifications. The query results of MQL are enriched with both textual and visual representations, and its query processing is completely tailored based on the underlying metabolic principles.

The "Cube-Query-Language" (CQL) is a new query language for flexible access to Multidimensional Database Systems. CQL provides a high level user interface for specifying queries in the context of multidimensional data analysis. The multidimensional view is widely accepted for typical decision support requirements like "Online Analytical Processing". CQL directly supports this view and circumvents the problems of formulating multiple groupby's and equijoins in a typical SQL-notation. Furthermore, in comparison to currently available OLAP-solutions, CQL provides a SQL like query interface, making it easy for an experienced SQL-programmer, to formulate decision support queries efficiently.

The paper details the two-step query processing found in the CQL approach: "data querying" and "data presentation". This description of the query language design is accompanied by a lot of examples, stemming from a joint research project with our industrial partner, thus showing the expressive power and flexibility of the query language design.