With the rapid proliferation of multimedia applications that require video data management, it is becoming more desirable to provide proper video data indexing techniques capable of representing the rich semantics in video data. In real-time applications, the need for efficient query processing is another reason for the use of such techniques. We present models that use the object motion information in order to characterize the events to allow subsequent retrieval. Algorithms for different spatiotemporal search cases in terms of spatial and temporal translation and scale invariance have been developed using various signal and image processing techniques. We have developed a prototype video search engine, PICTURESQUE (pictorial information and content transformation unified retrieval engine for spatiotemporal queries) to verify the proposed methods. Development of such technology will enable true multimedia search engines that will enable indexing and searching of the digital video data based on its true content.

Objective Rule-based data quality assessment in health care facilities was explored through compilation, implementation, and evaluation of 63,397 data quality rules in a single-center case study to assess the ability of rules-based data quality assessment to identify data errors of importance to physicians and system owners. Methods We applied a design science framework to design, demonstrate, test, and evaluate a scalable framework with which data quality rules can be managed and used in health care facilities for data quality assessment and monitoring. Results We identified 63,397 rules partitioned into 28 logic templates. A total of 819,683 discrepancies were identified by 4.5% of the rules. Nine out of 11 participating clinical and operational leaders indicated that the rules identified data quality problems and articulated next steps that they wanted to take based on the reported information. Discussion The combined rule template and knowledge table approach makes governance and maintenance of otherwise large rule sets manageable. Identified challenges to rule-based data quality monitoring included the lack of curated and maintained knowledge sources relevant to data error detection and lack of organizational resources to support clinical and operational leaders with investigation and characterization of data errors and pursuit of corrective and preventative actions. Limitations of our study included implementation within a single center and dependence of the results on the implemented rule set. Conclusion This study demonstrates a scalable framework (up to 63,397 rules) with which data quality rules can be implemented and managed in health care facilities to identify data errors. The data quality problems identified at the implementation site were important enough to prompt action requests from clinical and operational leaders.

In this paper, we propose a graphical data model for specifying spatio-temporal semantics of video data. The proposed model segments a video clip into subsegments consisting of objects. Each object is detected and recognized, and the relevant information of each object is recorded. The motions of objects are modeled through their relative spatial relationships as time evolves. Based on the semantics provided b y this model, a user can create his/her own object-oriented view of the video database. Using ihe propositional logic, we describe a methodology f o r specifying conceptual queries involving spatio-temporal semantics and expressing views f o r retrieving various video clips. Alternatively, a user can sketch the query, by examplifying the concept. The proposed methodology can be used to specify spatio-temporal concepts at various levels of information granularity.

This paper presents a framework for data modeling and semantic abstraction of image/video data. The framework is based on spatio-temporal information associated with salient objects in an image or in a sequence of video frames and on a set of generalized n-ary operators defined to specify spatial and temporal relationships of objects present in the data. The methodology presented in this paper can manifest itself effectively in conceptualizing events and heterogeneous views an multimedia data as perceaved by individual users. The proposed paradigm induces a multilevel indexing and searching mechanism that models information at various levels of granularity and hence allows processing of content-based queries in real time. We also devise a unified object-oriented interface for users with heterogeneous views to specify queries on the unbiased encoded data. Currently this framework is being developed to realize a highly integrated multimedia database architecture. '