Patent Application
20070192351
The present disclosure generally relates to distributed surveillance systems, and relates in particular to metadata schema mapping and data storage for use with distributed surveillance systems.
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Distributed video surveillance and sensor network systems usually contain varieties of devices and generate a great amount of media data and metadata information continuously. To incorporate all these types of equipments, store information, and retrieve data requires technologies including data definition schema translation and mapping from XML to Relational, query transformation of XPath/XQuery to SQL, and multilevel content representation for complex data models.
The mechanisms used for data definition schema transformation include structure mapping and model mapping. There are different methods being proposed to solve the problems: Structure mapping, including “basic, shared & hybrid inlining technique”, “X-Ray”, “XML-DBMS”, and “Cost-based approach”; and Model mapping, including “Edge approach”, “Monet”, “Xrel”, and “XParent”. Each method has strengths to solve one or more types of schema, but all have different advantages and disadvantages.
A metadata database adaptor for use with a surveillance and/or sensor system is capable of adapting metadata messages of varying formats according to needs of metadata databases. In other aspects, a metadata data model template can allow a user to define a data definition that is used to map user defined data with the metadata data model template to form XML data schema. Template mapping knowledge can then be applied for fast XML data schema to non-XML database schema generation without translation processing. In yet other aspects, an application domain template can allow a user to define a query definition that can then be processed with a mapping of the application domain template to translate Xpath, Xquery, or others to a database query.
Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.
The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.
The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses.
Distributed video surveillance and sensor network systems usually incorporate a variety of devices in terms of device types, vendors, communication protocols, and data specifications. Some functions that can be needed for such surveillance and sensor systems is to acquire, store, search, and retrieve data efficiently. The challenge is to be faster, more flexible, easier-to-use, and more accurate.
In general, new data models and operation flows need to be defined when a new surveillance and sensor network system is created. Most of the surveillance and sensor devices and systems can communicate with others using XML data format although the data schema can be different, but are usually similar.
A template driven system can efficiently create and manage data definition schema as well as search and access operation query definitions for distributed surveillance and sensor network devices. In order to handle a variety of types and complex data formats, such as industrial standard MPEG-7 and SensorML information, the system framework takes advantages of using multi-modal schema transformation and provides open interfaces for plug-in of multiple schema mapping and query transformation engines. It also provides multi-stage query pre-processing and post-processing for handling complex queries. In addition, it can be equipped with the capability to enhance template contents automatically via observing (mining) from user's operation historical data, creating indexes for data schema, and adding query statements. Templates can include application domain data model, data schema definition, query definition, index definitions, and data rendering mechanisms.
The data definition schema mapping mechanism in this system can allow multiple methods to plug-in and select it by dynamic declaration. It is time consuming process to define, translate, and test for a correct schema for a new system. Using a template approach can reduce the effort of the process while avoiding errors, and provide more high performance system. Pre-defined and tested generic templates can be used for different styles of schema and queries with proper indexes. When a new application data schema plan is being created, the system can take inputs from an administrator and process them with template knowledge and available plug-in methods to map to one or more predefined DB schema formats without time-consuming processes. A similar concept is used for query operations.
Since the system framework can have the capability to allow plug-in of different schema mapping and query transformation processor modules, it can handle many styles of schema, from simple definitions of simple devices to complex definitions like MPEG-7, and different query languages like XPath, XQuery, SQL, etc. to fit all the needs of video surveillance and sensor network applications.
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According to the example configuration, there are two types of Metadata DB Adaptors in the distributed system: Regular DB Adaptor, such as adaptors 134-136; and Master DB Adaptor, such as adaptor 138. Both types of database adaptors can contain interfaces 140, service managers 142, functional modules 144, database managers 146, and database connection pools 148. In one respect, the regular database adaptor and master database adaptor differ in that the service manager of the regular database adaptor is an adaptor service manager, while the service manager of the master database adaptor is a master service manager. In another respect, the two types of adaptors differ in the types of their functional modules. Specifically, both types of adaptors can have query mapping manager modules and data processing manager modules. However, the regular database adaptor can have a collection templates manager module, a schema mapping manager module, and an index manager module. In contrast, the master database adaptor can have an adaptor directory module, a collection schema directory manager module, and a collection template directory manager module.
Metadata databases 128 and 130 can differ from master metadata database 132. In particular, databases 128 and 130 can each store collection information, data tables, query statements, and index metadata. In contrast, master metadata database 132 can store a template database, an adaptor directory, a collection directory, and rules and configurations.
The database management system (DBMS) can be any kind (relational, object, object-relational, file, etc.). SQL and relational DB schema are used as examples herein, but it should be readily understood that any kind of DBMS combined with the template approach can save effort and time for users and for field deployment by reducing errors and facilitating the deployment process.
The functional modules of regular metadata database adaptors 134-136 can have specialized functions and, in some cases, be designed to interact with modules of the master metadata database adaptor. For example, a collection template manager module can manage collection creation, modification, and deletion in a local metadata DB, and post collection registration to remote master DB adaptor 138. Also, a schema mapping manager module can handle mapping, translation, and generation of data definition schema to XML schema and to database data schema like relational DB schema. If the data definition schema is based on an existing template, the process time can be reduced using pre-processed mapping stored in the template. Schema mapping manager module can also contain a shared memory module to store active schema mapping metadata for other operation modules to use. It can reduce the re-processing time for each operation, and provide high performance schema processing and generation. Further, an index manager module can provide DB table index creation and deletion management. It can also have an intelligent mining feature to look into query operation historical data and learn from query pattern data and search operation time. It can determine if a new index needs to be created for enhancing the search performance when the historical data of search operation time are over a pre-defined threshold.
Modules common to both types of adaptors can provide functionalities useful to both types of modules. For example, a query mapping manager can handle mapping, translation, and generation of query criteria to XPath/XQuery and to database query languages like SQL. It can save a great amount of time if the query criteria are based on the existing template. Query manager module can also have an intelligent mining feature to look into query operation historical data and learn from query pattern data to determine if a new query template is needed to generate query criteria.
Functional modules unique to the master database adaptor can provide several directory services. For example, these modules can include an adaptor directory, a collection template directory, a schema directory, a query template directory, etc.
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AV media metadata 522 can be especially rich. For example, content 544 can include video 572, sensor content 574, audio 576, other content 578, and image 580. Also, video 572 can include media occurrence 582, temporal segment 584, spatial segment 586, and spatio temporal segment 588, while each of segment 584-588 can include instances of description 590-594. Further, event 546 can include ID 596, type 598, cause 600, source 602, date/time 604, status 606, UID 608, and action 610. Still further, description 548 can include metadata 611, visual description 612, tracking description 614, audio description 616, NEWDevice_Desc 618, and object description 620, while EventContent_Relation 550 can include the relation 622. Finally, Video_Thumbnail_Metadata 552 can include ID 624, Image_Property 626, Image_URL 628, Content_ID 630, and Segment_ID 632.
In general, video surveillance systems produce and interact with video media data and video analysis data. MPEG-7 is one of the industry standards for describing multimedia information of media including video, and it is used by many video analysis and recognition systems to describe the media information. Due to the complexity of MPEG-7 data definition schema, designing an efficient MPEG-7 information data repository system is a challenge.
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The systems and methods described above provide several advantages over prior systems and methods. For example, these systems and methods provide an easy and efficient field programmable platform to create and manage metadata structure for DB storage. Also, template driven mapping can reduce the difficulties and errors in creation of surveillance and sensor device metadata schema. Further a multi-modal mechanism in data definition schema transformation to DB storage can achieve high performance access and retrieval. Yet further, multi-level indexing and a multi-stage query processes achieve both efficient search and accurate search needs. Further still, the systems and methods can easily add/remove new types of video surveillance devices or sensor devices with minimum metadata collection configuration for different application domains. Finally, intelligent self-learning query discovery and mining mechanisms can be included for distributed surveillance and sensor data.
