Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13]. / This is an extended version of the article in: 15th International Conference on Data Engineering, Sydney, Australia, 1999.

Concept-oriented model of data (COM) has been recently defined syntactically by means of the concept-oriented query language (COQL). In this paper we propose a formal embodiment of this model, called nested partially ordered sets (nested posets), and demonstrate how it is connected with its syntactic counterpart. Nested poset is a novel formal construct that can be viewed either as a nested set with partial order relation established on its elements or as a conventional poset where elements can themselves be posets. An element of a nested poset is defined as a couple consisting of one identity tuple and one entity tuple. We formally define main operations on nested posets and demonstrate their usefulness in solving typical data management and analysis tasks such as logic navigation, constraint propagation, inference and multidimensional analysis.

The widespread success of data analysis in a growing number of application domains has lead to the development of a variety of purpose build data processing systems. Today, many organizations operate whole fleets of different data related systems. Although this differentiation has good reasons there is also a growing need to create holistic perspectives that cut across the borders of individual systems. Application experts that want to create such perspectives are confronted with a variety of programming interfaces, data formats, and the task to combine available systems in an efficient manner. These issues are generally unrelated to the application domain and require a specialized set of skills. As a consequence, development is slowed down and made more expensive which stifles exploration and innovation. In addition, the direct use of specialized system interfaces can couple application code to specific processing systems. In this dissertation, we propose the data processing platform DataCalc which presents users with a unified application oriented programming interface and which automatically executes this interface in an efficient manner on a variety of processing systems. DataCalc offers a managed environment for data analyses that enables domain experts to concentrate on their application logic and decouples code from specific processing technology. The basis of this managed processing environment are the high-level domain oriented program representation DCIL and a flexible and extensible cost based optimization component. In addition to traditional up-front optimization, the optimizer also supports dynamic re-optimization of partially executed DCIL programs. This enables the system to benefit from dynamic information that only becomes available during execution of queries. DataCalc assigns workloads to available processing systems using a fine grained task scheduling model to enable efficient exploitation of available resources. In the second part of the dissertation we present a prototypical implementation of the DataCalc platform which includes connectors for the relational DBMS PostgreSQL, the document store MongoDB, the graph database Neo4j, and for the custom build PyProc processing system. For the evaluation of this prototype we have implemented an extended application scenario. Our experiments demonstrate that DataCalc is able to find and execute efficient execution strategies that minimize cross system data movement. The system achieves much better results than a naive implementation and it comes close to the performance of a hand-optimized solution. Based on these findings we are confident to conclude that the DataCalc platform architecture provides an excellent environment for cross domain data analysis on a heterogeneous federated processing architecture.

Concept-oriented model of data (COM) has been recently defined syntactically by means of the concept-oriented query language (COQL). In this paper we propose a formal embodiment of this model, called nested partially ordered sets (nested posets), and demonstrate how it is connected with its syntactic counterpart. Nested poset is a novel formal construct that can be viewed either as a nested set with partial order relation established on its elements or as a conventional poset where elements can themselves be posets. An element of a nested poset is defined as a couple consisting of one identity tuple and one entity tuple. We formally define main operations on nested posets and demonstrate their usefulness in solving typical data management and analysis tasks such as logic navigation, constraint propagation, inference and multidimensional analysis.

Processing data streams with Quality-of-Service (QoS) guarantees is an emerging area in existing streaming applications. Although it is possible to negotiate the result quality and to reserve the required processing resources in advance, it remains a challenge to adapt the DSMS to data stream characteristics which are not known in advance or are difficult to obtain. Within this paper we present the second generation of our QStream DSMS which addresses the above challenge by using a real-time capable operating system environment for resource reservation and by applying an adaptation mechanism if the data stream characteristics change spontaneously.

Domain-specific query languages (DSQL) let users express custom business logic. Relational databases provide a limited set of options to execute business logic. Usually, stored procedures or a series of queries with some glue code. Both methods have drawbacks and often business logic is still executed on application side transferring large amounts of data between application and database, which is expensive. We translate a DSQL into a hybrid data-flow execution plan, containing relational operators mixed with procedural ones. A cost model is used to drive the translation towards an optimal mixture of relational and procedural plan operators.

As databases accumulate growing amounts of data at an increasing rate, adaptive indexing becomes more and more important. At the same time, applications and their use get more agile and flexible, resulting in less steady and less predictable workload characteristics. Being inert and coarse-grained, state-of-the-art index tuning techniques become less useful in such environments. Especially the full-column indexing paradigm results in many indexed but never queried records and prohibitively high storage and maintenance costs. In this paper, we present Self-Managing Indexes, a novel, adaptive, fine-grained, autonomous indexing infrastructure. In its core, our approach builds on a novel access path that automatically collects useful index information, discards useless index information, and competes with its kind for resources to host its index information. Compared to existing technologies for adaptive indexing, we are able to dynamically grow and shrink our indexes, instead of incrementally enhancing the index granularity.