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Quality of Service (QoS) is defined as a set of perceivable attributes expressed in a user-friendly language with parameters that may be subjective or objective. Objective parameters are those related to a particular service and are measurable and verifiable. Subjective parameters are those based on the opinions of the end-users. We believe that qu...
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Context 1
... abstract architecture, inspired from the one given in [13], is designed to provide a man- agement system for video data. Figure 2 shows the block level architecture of a video data management system integrating quality of service management. The function of each of these blocks is described below. ...
Context 2
... appearing in the view part of a query may be divided into "local" quality pred- icates and "external" quality predicates. "external" quality predicates are those involving data of the video representation parameters store (see figure 2). ...
Context 3
... takes place in the model when objects invoke named predicates in the interfaces of other objects. Hence in our architecture (see figure 2) each module is considered as an object encapsulating a set of internal operations. Additionally, we have two other important objects, namely Network and OS (Operating System). ...
Citations
... In that paper, we extend the query processing/ optimization module of a multimedia DBMS to handle quality in queries as a core DBMS functionality. Two other related works in multimedia databases discuss quality specification [11] and quality model [12]. None of the above deals with replication of copies with different qualities. ...
... Yet, it is different from any known UFL problems in that the storage constraint in FSRS is unique. A close match to FSRS is the so-called p-median problem with the same problem statements, except (11) becomes P X k ¼ p, meaning only p ðp < jJjÞ facilities are to be built. As the p-median problem is NP-hard [29], we can thus conclude FSRS is also NP-hard. ...
... 10 We run the experiments for a total of 30 media objects. 11 Each data point represents the mean of four simulations. ...
In contrast to other database applications, multimedia data can have a wide range of quality parameters, such as spatial and temporal resolution and compression format. Users can request data with specific quality requirements due to the needs of their application or the limitations of their resources. The database can support multiple qualities by converting data from the original (high) quality to another (lower) quality to support a user's query or precompute and store multiple quality replicas of data items. On-the-fly conversion of multimedia data (such as video transcoding) is very CPU intensive and can limit the level of concurrent access supported by the database. Storing all possible replicas, on the other hand, requires unacceptable increases in storage requirements. In this paper, we address the problem of multiple-quality replica selection subject to an overall storage constraint. We establish that the problem is NP-hard and provide heuristic solutions under two different system models: hard-quality and soft-quality. Under the soft-quality model, users are willing to negotiate their quality needs, as opposed to the hard-quality system wherein users can only accept the exact quality requested. Extensive simulations show that our algorithm performs significantly better than other heuristics. Our algorithms are flexible in that they can be extended to deal with changes in query pattern
... We define a mathematical notation that allows formal representation of our access control subjects, objects and authorizations. This formalism makes heavy use of the conventions of set theory and builds on authorization models described in [11, 4]. ...
In this paper we have addressed confidentiality and privacy for video surveillance databases. First we discussed our overall approach for suspicious event detection. Next we discussed an access control model and accedes control algorithms for confidentiality. Finally we discuss privacy preserving video surveillance. Our goal is build a comprehensive system that can detect suspicious events, ensure confidentiality as well as privacy.
... The quality parameters of interest also differ by the type of media that we deal with. For digital videos, which we use as example throughout this paper, the quality parameters include resolution, frame rate, color depth, audio quality, compression format, security level, and so on [4]. For example, a video editor may request a video at very high resolution when editing it on a highpowered workstation, but request the video at low resolution and frame rate when viewing it using a PDA. ...
... In our previous work [1], we extend the query generation/optimization module of a multimedia DBMS to handle quality of queries as a core DBMS functionality. In [4] , specification of quality parameters in multimedia databases is discussed. The traditional data replication problem has been studied extensively in the context of web [10,11], distributed databases [8], and multimedia systems [12,13]. ...
In contrast to alpha-numerical data, multimedia data can have a wide range of quality parameters such as spatial and temporal resolution, and compression format. Users can request data with a specific quality requirement due to the needs of their applications, or the limitations of their resources. On-the-fly conversion of multimedia data (such as video transcoding) is very CPU intensive and can limit the level of concurrent access supported by the database. Storing all possible replicas, on the other hand, requires unacceptable increases in storage requirements. Although replication has been well studied, to the best of our knowledge, the problem of multiple-quality replication has not been addressed. In this paper we address the problem of multiple-quality replica selection subject to an overall storage constraint.
We establish that the problem is NP-hard and provide heuristic solutions under a soft quality system model where users are willing to negotiate their quality needs. An important optimization goal under such a model is to minimize utility loss. We propose a powerful greedy algorithm to solve this optimization problem. Extensive simulations show that our algorithm finds near-optimal solutions. The algorithm is flexible in that it can be extended to deal with replica selection for multiple media objects and changes of query pattern. We also discuss an extended version of the algorithm with potentially better performance.
... In the context of general multimedia system, research on QoS has concentrated on system and network support with little concern for QoS control on the higher (user, application) levels. High-level QoS support is essential in any multimedia systems because the satisfaction of human users is the primary concern in defining QoS [2]. Simply deploying a multimedia database system on top of a QoS-provisioning system will not provide end-to-end user-level QoS. ...
... As shown in Figure 6, QuaSAQ augments VDBMS and sits between Shore and PREDATOR in the query processing path. In our QuaSAQ-enhanced database, queries on videos are processed in two steps: 1. searching and identification of video objects done by the original VDBMS; 2. QoSconstrained delivery of the video by QuaSAQ [2]. To identify storage items (raw video, indices, relations, etc.) throughout the system, a 12-byte ID is assigned to each object according to the Shore convention. ...
... The design and realization of QuaSAQ is motivated by the high-level concepts sketched in a previous work [2]. The main contribution of [2] is to specify QoS in video database queries by a query language based on constraint logic pro-gramming. ...
The paper discusses the design and prototype implementa- tion of a QoS aware multimedia database system. Recent research in multimedia databases has devoted little atten- tion to the aspect of the integration of QoS support at the user level. One common scenario which we are concerned with connects a user through a visual interface to a multi- tude of media object stores. The user demands satisabil- ity of a set of quality parameter bounds specied at query time or before (via user prole mappings). The user is not aware of detailed low-level QoS parameters but rather spec- ies high-level, qualitative attributes on the media query. Our proposed architecture to enable end-to-end QoS control, the QoS-Aware Query Processor (QuaSAQ), is motivated by query processing and optimization techniques in traditional database management systems. The proposed solution relies on mediation through several components (two of which are the QoP Browser and Quality Manager) that enable search- ing, locating, composing and presenting of multimedia ob- jects with associated QoS constraints. In addition to an overview of key research issues, this paper also presents some of the proposed design solutions. One focus problem is how to evaluate the alternative plans for serving QoS-enhanced queries. We propose a novel cost model that explicitly takes the resource utilization of plans and the current system con- tention level into account. Experiments run on the QuaSAQ prototype show signican tly improved QoS and throughput in media query processing.
Quality is an essential property for multimedia databases. In contrast to other database applications, multimedia data can have a wide range of quality parameters such as spatial and temporal resolution, and compression format. Users can request data with a specic quality requirement due to the needs of their application, or the limitations of their resources. The database can support multiple qualities by converting data from the original (high) quality to another (lower) quality to support a user's query, or pre-compute and store multiple quality replicas of data items. On-the-y conversion of multimedia data (such as video transcoding) is very CPU intensive and can limit the level of concurrent ac- cess supported by the database. Storing all possible replicas, on the other hand, requires unacceptable increases in storage requirements. Although replication has been well studied, to the best of our knowledge, the problem of multiple-quality replication has not been addressed. In this paper we address the problem of multiple-quality replica selection subject to an overall storage constraint. We establish that the problem is NP-hard and provide heuristic solutions under two dieren t system models: Hard- Quality, and Soft-Quality. Under the soft-quality model, users are willing to negotiate their quality needs, as opposed to the hard-quality system wherein users will only accept the exact quality requested. The hard-quality problem is reduced to a 0-1 Knapsack problem and we propose an e- cient solution that minimizes the probability of request re- jection due to unavailability of the requested quality replica. For the soft-quality system, an important optimization goal is to minimize utility loss. We propose a powerful greedy al- gorithm to solve this problem. Extensive simulations show that our algorithm performs signican tly better than other heuristics. The algorithm is exible in that it can be ex- tended to deal with problems of distributed data replication and changes in query pattern.
This system description … QuAMobile has a small core with hooks for QoS management plug-ins. The plug-ins manages QoS on behalf of the application, provided that the application is deployed together with information about alternative application configurations and their QoS characteristics. Applications are modelled as compositions of services, where each service is defined by a service type. For each service type there is one or more alternative implementations, i.e., component compositions and parameter configurations.
The paper discusses the design and prototype implementa- tion of a QoS-aware multimedia database system. Recent research in multimedia databases has devoted little attention to the aspect of the integration of QoS support at the user level. Our proposed architec- ture to enable end-to-end QoS control, the QoS-Aware Query Processor (QuaSAQ), satises user specied quality requirements. The users need not be aware of detailed low-level QoS parameters, but rather species high-level, qualitative attributes. In addition to an overview of key re- search issues in the design of QoS-aware databases, this paper presents our proposed solutions, and system implementation details. An impor- tant issue relates to the enumeration and evaluation of alternative plans for servicing QoS-enhanced queries. This step follows the conventional query execution which results in the identication of objects of interest to the user. We propose a novel cost model for media delivery that ex- plicitly takes the resource utilization of the plan and the current system contention level into account. Experiments run on the QuaSAQ proto- type show signican tly improved QoS and system throughput.
The increased use of video data sets for multimedia-based applications has created a demand for strong video database support, including efficient methods for handling the contentbased query and retrieval of video data. Video query processing presents significant research challenges, mainly associated with the size, complexity and unstructured nature of video data. A video query processor must support video operations for search by content and streaming, new query types, and the incorporation of video methods and operators in generating, optimizing and executing query plans. In this paper, we address these query processing issues in two contexts, first as applied to the video data type and then as applied to the stream data type. We present the query processing functionality of the VDBMS video database management system, which was designed to support a full range of functionality for video as an abstract data type. We describe two query operators for the video data type which implement the rank-join and stop-after algorithms. As videos may be considered streams of consecutive image frames, video query processing can be expressed as continuous queries over video data streams. The stream data type was therefore introduced into the VDBMS system, and system functionality was extended to support general data streams. From this viewpoint, we present an approach for defining and processing streams, including video, through the query execution engine. We describe the implementation of several algorithms for video query processing expressed as continuous queries over video streams, such as fast forward, region-based blurring and left outer join. We include a description of the window-join algorithm as a core operator for continuous query systems, and discuss shared execution as ...
