Rule-based spatiotemporal query processing for video databases

In our earlier work, we proposed an architecture for a Web-based video database management system (VDBMS) providing an integrated support for spatiotemporal and semantic queries. In this paper, we focus on the task of spatiotemporal query processing and also propose an SQL-like video query language that has the capability to handle a broad range of spatiotemporal queries. The language is rule-based in that it allows users to express spatial conditions in terms of Prolog-type predicates. Spatiotemporal query processing is carried out in three main stages: query recognition, query decomposition, and query execution.Received: 11 October 2001, Accepted: 3 October 2003, Published online: 12 December 2003Edited by: A. Buchmann Correspondence to: Özgür UlusoyThis work is supported by the Scientific and Research Council of Turkey (TÜBITAK) under Project Code 199E025. This work was done while the first author was at Bilkent University.     

Query-by-Gaming: Interactive spatio-temporal querying and retrieval using gaming controller

Spatio-temporal querying and retrieval is a challenging task due to the lack of simple user interfaces for building queries despite the availability of powerful indexing structures and querying languages. In this paper, we propose Query-by-Gaming scheme for spatio-temporal querying that can benefit from gaming controller for building queries. By using Query-by-Gaming, we introduce our spatio-temporal querying and retrieval system named as GStar to interactively build subsequent spatio-temporal queries to determine if a state is directly reachable from current state and eventual spatio-temporal queries to know whether a spatial state is reachable from a current state. Queries are built using features of gaming controller by displaying the original video frames rather than on a graphical interface using a mouse or a keyboard. GStar has three main components: building the query, searching and retrieval of clips, and displaying query results. The queries are applied to an indexing structure called semantic sequence state graph (S³G) and results of the query are displayed dynamically to provide timely feedback to the user. Experimental results and user interface are provided for a tennis video database. Users define desired game state (player and ball position) using an interactive interface at multiple points in time and GStar automatically retrieves all rallies that contain both states. Finally, the user interface evaluation comparing gamepad-based interface and mouse interface for spatio-temporal querying has been studied.     

SNMPv3代理在网络视频监控终端的嵌入式实现

网络视频监控是未来安防监控领域的主流技术.SNMPv3是简单网络管理协议的第3版,在原有SNMP协议基础上增添了基于用户的安全模型和基于视图的访问控制模型,加强了协议的安全性.通过在网络视频监控终端中嵌入SNMPv3代理,实现了对网络视频监控系统的统一、安全、有效管理.     

语义视频检索的现状和研究进展

概述了图像的可视化特征如颜色、纹理、形状和运动信息,时空关系分析,以及多特征目标提取和相似度量度;分析了视频语义的提取,语义查询、检索;探讨了视频语义检索的性能评估,存在的问题和发展方向。     

A Semantic Cache Framework for Secure XML Queries

Secure XML query answering to protect data privacy and semantic cache to speed up XML query answering are two hot spots in current research areas of XML database systems. While both issues are explored respectively in depth,they have not been studied together,that is,the problem of semantic cache for secure XML query answering has not been addressed yet. In this paper,we present an interesting joint of these two aspects and propose an efficient framework of semantic cache for secure XML query answering,which can improve the performance of XML database systems under secure circumstances. Our framework combines access control,user privilege management over XML data and the state-of-the-art semantic XML query cache techniques,to ensure that data are presented only to authorized users in an efficient way. To the best of our knowledge,the approach we propose here is among the first beneficial efforts in a novel perspective of combining caching and security for XML database to improve system performance. The efficiency of our framework is verified by comprehensive experiments.     

Hybrid query execution engine for large attributed graphs

Graphs are widely used for modeling complicated data such as social networks, bibliographical networks and knowledge bases. The growing sizes of graph databases motivate the crucial need for developing powerful and scalable graph-based query engines. We propose a SPARQL-like language, G-SPARQL, for querying attributed graphs. The language enables the expression of different types of graph queries that are of large interest in the databases that are modeled as large graph such as pattern matching, reachability and shortest path queries. Each query can combine both structural predicates and value-based predicates (on the attributes of the graph nodes/edges). We describe an algebraic compilation mechanism for our proposed query language which is extended from the relational algebra and based on the basic construct of building SPARQL queries, the Triple Pattern. We describe an efficient hybrid Memory/Disk representation of large attributed graphs where only the topology of the graph is maintained in memory while the data of the graph are stored in a relational database. The execution engine of our proposed query language splits parts of the query plan to be pushed inside the relational database (using SQL) while the execution of other parts of the query plan is processed using memory-based algorithms, as necessary. Experimental results on real and synthetic datasets demonstrate the efficiency and the scalability of our approach and show that our approach outperforms native graph databases by several factors.     

Hierarchical database for a multi-camera surveillance system

This paper presents a framework for event detection and video content analysis for visual surveillance applications. The system is able to coordinate the tracking of objects between multiple camera views, which may be overlapping or non-overlapping. The key novelty of our approach is that we can automatically learn a semantic scene model for a surveillance region, and have defined data models to support the storage of tracking data with different layers of abstraction into a surveillance database. The surveillance database provides a mechanism to generate video content summaries of objects detected by the system across the entire surveillance region in terms of the semantic scene model. In addition, the surveillance database supports spatio-temporal queries, which can be applied for event detection and notification applications.     

Query optimization in information integration

The problem of decentralized data sharing, which is relevant to a wide range of applications, is still a source of major theoretical and practical challenges, in spite of many years of sustained research. In this paper we focus on the challenge of efficiency of query evaluation in information integration systems that use the global-as-view approach, with the objective of developing query-processing strategies that would be widely applicable and easy to implement in real-life applications. Our algorithms take into account important features of today’s data sharing applications: XML as likely interface or representation for data sources; the potential for information overlap across data sources; and the need for inter-source processing, as in joins of data across sources. The focus of this paper is on performance-related characteristics of several alternative approaches that we propose for efficient query processing in information integration, including an approach that uses materialized restructured views. We use synthetic and real-life datasets in our implementation of an information integration system shell to provide experimental results that demonstrate that our algorithms are efficient and competitive in the information integration setting. In addition, our experimental results allow us to make context-specific recommendations on selecting query-processing approaches from our proposed alternatives. As such, our approaches could form a basis for scalable query processing in information integration and interoperability in many practical settings.     

A novel XML keyword query approach using entity subtree

Keyword query is an important means to find object information in XML document. Most of the existing keyword query approaches adopt the subtrees rooted at the smallest lowest common ancestors of the keyword matching nodes as the basic result units. The structural relationships among XML nodes are excessively emphasized but the semantic relevance is not fully exploited.To change this situation, we propose the concept of entity subtree and emphasis the semantic relevance among different nodes as querying information from XML. In our approach, keyword query cases are improved to a new keyword-based query language, Grouping and Categorization Keyword Expression (GCKE) and the core query algorithm, finding entity subtrees (FEST) is proposed to return high quality results by fully using the keyword semantic meanings exposed by GCKE. We demonstrate the effectiveness and the efficiency of our approach through extensive experiments.     

Savanta—search,analysis, visualisation and navigation of temporal annotations

In this article, we present Savanta, an information gathering interface for temporal, semantic video annotations. In Savanta, we integrate various methods and paradigms for information gathering, including visualisation, filtering, data mining, navigation and search, in order to explore the possible advantages of doing so. We posit that a seamless integration of multiple access methods, combined with an improved interval visualisation scheme and dynamically generated metadata, will result in greater user satisfaction compared to conventional approaches for searching and querying video databases—despite the increased complexity that may result. We perform a formal usability evaluation comparing Savanta to systems based on traditional search/query paradigms, and conclude that Savanta outperforms them with regard to both power and usability, especially for complex and open-ended tasks.     

SVAS: Surveillance Video Analysis System

This paper introduces a Surveillance Video Analysis System, called SVAS, for surveillance domain, in which the semantic rules and the definition of event models can be learned or defined by the user for automatic detection and inference of complex video events. In the scope of SVAS, an event model method named Interval-Based Spatio-Temporal Model (IBSTM) is proposed. SVAS can learn action models and event models without any predefined threshold values and generates understandable and manageable IBSTM event models. Hybrid machine learning methods are proposed and used. A set of feature models named Threshold Model, which reflects the spatio-temporal motion analysis of an event, is kept as the first model. As the second model, Bag of Actions (BoA) model is used in order to reduce the search space in the detection phase. Markov Logic Network (MLN) model, which provides understandable and manageable logic predicates for users, is kept as the third model. SVAS has high performance event detection capability due to its interval-based hierarchical manner. It determines related candidate intervals for each main model of IBSTM and uses the related main model when needed rather than using all models as a whole. The main contribution of this study is to fill the semantic gap between humans and video computer systems such that, on the one hand it decreases human intervention through its learning capabilities, but on the other hand it also enables human intervention when necessary through its manageable event model method. The study achieves all of them in the most efficient way through its machine learning methods. The proposed system is applied to different event datasets from CAVIAR, BEHAVE and our synthetic datasets. The experimental results show that our approach improves the event recognition performance and precision as compared to the current state-of-the-art approaches.     

Semantic-aware top-k spatial keyword queries

The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword query methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial Web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose novel indexing structures, which integrate spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare them with other baseline algorithms.     

Spatio-temporal querying in video databases

A video data model that supports spatio-temporal querying in videos is presented. The data model is focused on the semantic content of video streams. Objects, events, activities, and spatial properties of objects are main interests of the model. The data model enables the user to query fuzzy spatio-temporal relationships between video objects and also trajectories of moving objects. A prototype of the proposed model has been implemented.     

MedSMan: a live multimedia stream querying system

Querying live media streams is a challenging problem that is becoming an essential requirement in a growing number of applications. Research in multimedia information systems has addressed and made good progress in dealing with archived data. Meanwhile, research in stream databases has received significant attention for querying alphanumeric symbolic streams. The lack of a data model capable of representing different multimedia data in a declarative way, hiding the media heterogeneity and providing reasonable abstractions for querying live multimedia streams poses the challenge of how to make the best use of data in video, audio and other media sources for various applications. In this paper we propose a system that enables directly capturing media streams from sensors and automatically generating more meaningful feature streams that can be queried by a data stream processor. The system provides an effective combination between extendible digital processing techniques and general data stream management research. Together with other query techniques developed in related data stream management streams, our system can be used in those application areas where multifarious live media senors are deployed for surveillance, disaster response, live conferencing, telepresence, etc.

Dynamic Query Optimization Approach for Semantic Database Grid

Fundamentally, semantic grid database is about bringing globally distributed databases together in order to coordinate resource sharing and problem solving in which information is given well-defined meaning, and DartGrid II is the implemented database gird system whose goal is to provide a semantic solution for integrating database resources on the Web. Although many algorithms have been proposed for optimizing query-processing in order to minimize costs and/or response time, associated with obtaining the answer to query in a distributed database system, database grid query optimization problem is fundamentally different from traditional distributed query optimization. These differences are shown to be the consequences of autonomy and heterogeneity of database nodes in database grid. Therefore, more challenges have arisen for query optimization in database grid than traditional distributed database. Following this observation, the design of a query optimizer in DartGrid II is presented, and a heuristic, dynamic and parallel query optimization approach to processing query in database grid is proposed. A set of semantic tools supporting relational database integration and semantic-based information browsing has also been implemented to realize the above vision.     

Improving Ocean Data Services with Semantics and Quick Index

Massive ocean data acquired by various observing platforms and sensors poses new challenges to data mana-gement and utilization.Typically,it is difficult to find the desired data from the large amount of datasets efficiently and effectively.Most of existing methods for data discovery are based on the keyword retrieval or direct semantic reasoning,and they are either limited in data access rate or do not take the time cost into account.In this paper,we creatively design and implement a novel system to alleviate the problem by introducing semantics with ontologies,which is referred to as Data Ontology and List-Based Publishing (DOLP).Specifically,we mainly improve the ocean data services in the following three aspects.First,we propose a unified semantic model called OEDO (Ocean Environmental Data Ontology) to represent heterogeneous ocean data by metadata and to be published as data services.Second,we propose an optimized quick service query list (QSQL) data structure for storing the pre-inferred semantically related services,and reducing the service querying time.Third,we propose two algorithms for optimizing QSQL hierarchically and horizontally,respectively,which aim to extend the semantics relationships of the data service and improve the data access rate.Experimental results prove that DOLP outperforms the benchmark methods.First,our QSQL-based data discovery methods obtain a higher recall rate than the keyword-based method,and are faster than the traditional semantic method based on direct reasoning.Second,DOLP can handle more complex semantic relationships than the existing methods.