Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

Modern database applications show a growing demand for efficient and dynamic management of intervals, particularly for temporal and spatial data or for constraint handling. Common approaches require the augmentation of index structures which, however, is not supported by existing relational database systems. By design, the new Relational Interval Tree (RI-tree) employs built-in indexes on an as-they-are basis and is easy to implement. Whereas

The increasing use of temporal and spatial data in present-day relational systems necessitates an efficient support of joins on interval-valued attributes. Standard join algorithms do not support those data types adequately, whereas special approaches for interval joins usually require an augmenta-tion of the internal access methods which is not supported by existing relational systems. To overcome these problems we introduce new join algorithms for interval data. Based on the Relational Interval Tree, these algorithms can easily be im-plemented on top of any relational database system while providing excellent performance on joining intervals. As experimental results on an Oracle9i server show, the new techniques outperform existing relational methods for joining intervals significantly.

The design of external index structures for one- and multidimensional extended objects is a long and well studied subject in basic database research. Today, more and more commercial applications rely on spatial datatypes and require a robust and seamless integration of appropriate access methods into reliable database servers. This paper proposes an efficient, dynamic and scalable approach to manage one-dimensional interval sequences within off-the-shelf object-relational database systems. The presented technique perfectly fits to the concept of space-filling curves and, thus, generalizes to spatially extended objects in multidimensional data spaces. Based on the Relational Interval Tree, the method is easily embedded in modern extensible indexing frameworks and significantly outmatches Linear Quadtrees and Relational R-trees with respect to usability, concurrency, and performance. As demonstrated by our experimental evaluation on an Oracle server with real GIS and CAD data, the competing methods are outperformed by factors of up to 4.6 (Linear Quadtree) and 58.3 (Relational R-tree) for query response time.

Commercial relational database systems today provide only limited temporal support. To address the needs of applications requiring rich temporal data and queries, we have built TIP (Temporal Information Processor), a temporal extension to the Informix database system based on its DataBlade technology. Our TIP DataBlade extends Informix with a rich set of datatypes and routines that facilitate temporal modeling and querying. TIP provides both C and Java libraries for client applications to access a TIPenabled database, and provides end-users with a GUI interface for querying and browsing temporal data. 1 Introduction Our research in temporal data warehouses [9, 10] has led us to require a relational database system with full SQL as well as rich temporal support, in order to experiment with our temporal view-maintenance techniques. Most commercial relational database systems support only a DATE type (or its variants). An attribute of type DATE can be used to timestamp a tuple with...

In order to achieve efficient execution plans for queries comprising userdefined data types and predicates, the database system has to be provided with appropriate index structures, query processing methods, and optimization rules. Although available extensible indexing frameworks provide a gateway to seamlessly integrate user-defined access methods into the standard process of query optimization and execution, they do not facilitate the actual implementation of the access method itself. An internal enhancement of the database kernel is usually not an option for database developers.

. Intervals represent a fundamental data type for temporal, scientific, and spatial databases where time stamps and point data are extended to time spans and range data, respectively. For OLTP and OLAP applications on large amounts of data, not only intersection queries have to be processed efficiently but also general interval relationships including before, meets, overlaps, starts, finishes, contains, equals, during, startedBy, finishedBy, overlappedBy, metBy, and after. Our new algorithms use the Relational Interval Tree, a purely SQL-based and objectrelationally wrapped index structure. The technique therefore preserves the industrial strength of the underlying RDBMS including stability, transactions, and performance. The efficiency of our approach is demonstrated by an experimental evaluation on a real weblog data set containing one million sessions. 1

The efficient management of interval data represents a core requirement for many temporal and spatial database applications. With the Relational Interval Tree (RI-tree ), an efficient access method has been proposed to process interval intersection queries on top of existing objectrelational database systems. This paper complements that approach by effective and efficient models to estimate the selectivity and the I/O cost of interval intersection queries in order to guide the cost-based optimizer whether and how to include the RI-tree into the execution plan. By design, the models immediately fit to common extensible indexing/ optimization frameworks, and their implementations exploit the built-in statistics facilities of the database server. According to our experimental evaluation on an Oracle database, the average relative error of the estimated cost to the actual cost of index scans ranges from 0% to 23%, depending on the resolution of the persistent statistics and the size of the query objects.

Most data managed by existing, real-world database applications is time referenced. Often, two temporal aspects of data are of interest, namely valid time, when data is true in the mini-world, and transaction time, when data is current in the database, resulting in so-called bitemporal data. Like spatial data, bitemporal data thus has associated twodimensional regions. Such data is in part naturally nowrelative: some data is true until the current time, and some data is part of the current database state. So, unlike for spatial data, bitemporal data regions may grow continuously. Existing indices, e.g., B # - and R-trees, typically do not contend well with even small amounts of now-relative data. in contrast,

User-defined data types such as intervals require specialized access methods to be efficiently searched and queried. As database implementors cannot provide appropriate index structures and query processing methods for each con-ceivable data type, present-day object-relational database systems offer extensible indexing frameworks that enable developers to extend the set of built-in index structures by custom access methods. Although these frameworks permit a seam-less integration of user-defined indexing techniques into query processing they do not facilitate the actual implementation of the access method itself. In order to lev-erage the applicability of indexing frameworks, relational access methods such as the Relational Interval Tree (RI-tree), an efficient index structure to process inter-val intersection queries, mainly rely on the functionality, robustness and perform-ance of built-in indexes, thus simplifying the index implementation significantly. To investigate the behavior and performance of the recently released IBM DB2 in-dexing framework we use this interface to integrate the RI-tree into the DB2 server. The standard implementation of the RI-tree, however, does not fit to the narrow corset of the DB2 framework which is restricted to the use of a single index only. We therefore present our adaptation of the originally two-tree technique to the single index constraint. As experimental results with interval intersection que-ries show, the plugged-in access method delivers excellent performance compared to other techniques.