Recent work on querying data streams has focused on systems where newly arriving data is processed and continuously streamed to the user in real time. In many emerging applications, however, ad hoc queries and/or intermittent connectivity also require the processing of data that arrives prior to query submission or during a period of disconnection. For such applications, we have developed PSoup, a system that combines the processing of ad hoc and continuous queries by treating data and queries symmetrically, allowing new queries to be applied to old data and new data to be applied to old queries. PSoup also supports intermittent connectivity by separating the computation of query results from the delivery of those results. PSoup builds on adaptive query-processing techniques developed in the Telegraph project at UC Berkeley. In this paper, we describe PSoup and present experiments that demonstrate the effectiveness of our approach.

An important feature of many advanced active database prototypes is support for rules triggered by complex patterns of events. Their composite event languages provide powerful primitives for event-based temporal reasoning. In fact, with one important exception, their expressive power matches and surpasses that of sophisticated languages offered by Time Series Management Systems (TSMS), which have been extensively used for temporal data analysis and knowledge discovery. This exception pertains to temporal aggregation, for which, current active database systems offer only minimal support, if any. In this paper, we introduce the language TREPL, which addresses this problem. The TREPL prototype, under development at UCLA, offers primitives for temporal aggregation that exceed the capabilities of state-of-the-art composite event languages, and are comparable to those of TSMS languages. TREPL also demonstrates a rigorous and general approach to the definition of composite event language sema...

System for scalable event processing. We present a query language based on Cayuga Algebra for naturally expressing complex event patterns. We also describe several novel system design and implementation issues, focusing on Cayuga’s query processor, its indexing approach, how Cayuga handles simultaneous events, and its specialized garbage collector. 1.

. As new query interfaces emerge for accessing multimedia data, formal user studies are needed to evaluate the usability of such interfaces. In this paper, we present results from a user interface evaluation of our temporal visual query language (TVQL). TVQL is a novel direct manipulation query interface for specifying temporal relationship queries over temporal events such as video data. In our user study, we compare TVQL to a formsbased temporal query language (TForms). Our results indicate that while subjects took longer to learn TVQL than TForms, they were more efficient and more accurate in specifying temporal queries with the TVQL interface than with the TForms interface. KEYWORDS Temporal visual query language, temporal query filters, dynamic queries, user interface evaluation. 1. INTRODUCTION Recent advances in multimedia databases have introduced new multimedia query languages and interfaces empowering users to access multimedia data in novel ways such as querying images b...

Relational database systems and most objectoriented database systems provide support for queries. Usually these queries represent retrievals over sets or multisets. Many new applications for databases, such as multimedia systems and digital libraries, need support for queries on complex bulk types such as lists and trees. In this paper we describe an object-oriented query algebra for lists and trees. The operators in the algebra preserve the ordering between the elements of a list or tree, even when the result list or tree contains an arbitrary set of nodes from the original tree. We also present predicate languages for lists and trees which allow order-sensitive queries because they use pattern matching to examine groups of list or tree nodes rather than individual nodes. The ability to decompose predicate patterns enables optimizations that make use of indices.

The need to search for complex and recurring patterns in database sequences is shared by many applications. In this work, we discuss how to express and support efficiently sophisticated sequential pattern queries in relational database systems. Thus, we first

Time sequences appear in various application domainso Many applications require time sequences to be seen as continuous where implicit values can be derived from explicit values by arbitrary user-defined interpolation functions. This paper describes the implementation of an extended SELECT operator, o*, that retrieves implicit values from a discrete time sequence under various user-defined interpolation assumptions. The o* operator is efficiently supported by an indexing technique termed the IP-index. Possible optimizations of the o* operator are investigated and verified by experiments on SHORE. The o* operator is applicable to any 1-D sequence data.

A relation is an unordered collection of records. Often, however, there is an underlying order (e.g., a sequence of stock prices), and users want to pose queries that reflect this order (e.g., find a weekly moving average). SQL provides no support for posing such queries. In this paper, we show how a rich class of queries reflecting sort order can be naturally expressed and efficiently executed with simple extensions to SQL. 1.

this paper, we investigate the design and optimization of a query language capable of expressing and supporting efficiently the search for complex sequential patterns in database systems. Thus, we first introduce SQL-TS, an extension of SQL to express these patterns, and then we study how to optimize the queries for this language. We take the optimal text search algorithm of Knuth, Morris and Pratt, and generalize it to handle complex queries on sequences. Our algorithm exploits the interdependencies between the elements of a pattern to minimize repeated passes over the same data. Experimental results on typical sequence queries, such as double bottom queries, confirm that substantial speedups are achieved by our new optimization techniques

Many kinds of real-life data exhibit logical ordering among their data items and are thus sequential in nature. However, traditional online analytical processing (OLAP) systems and techniques were not designed for sequence data and they are incapable of supporting sequence data analysis. In this paper, we propose the concept of Sequence OLAP, or S-OLAP for short. The biggest distinction of S-OLAP from traditional OLAP is that a sequence can be characterized not only by the attributes ’ values of its constituting items, but also by the subsequence/substring patterns it possesses. This paper studies many aspects related to Sequence OLAP. The concepts of sequence cuboid and sequence data cube are introduced. A prototype S-OLAP system is built in order to validate the proposed concepts. The prototype is able to support “pattern-based” grouping and aggregation, which is currently not supported by any OLAP system. The implementation details of the prototype system as well as experimental results are presented.