Today, most commercial database systems provide some support for the management of temporal data, but the index support for efficiently accessing such data is rather limited. Existing access paths neglect the fact that time intervals are located on the timeline and have a duration, two important pieces of information for querying temporal data.

The efficient management of temporal data has become increasingly important for many database applications. Most commercial systems already allow the management of temporal data but the operational support for processing this data is still rather limited. One particular reason is that many extension proposals typically require considerable modifications of the underlying database engine. In this paper, we propose a lightweight solution where temporal operators are realized using a library of user-defined functions. This way the complexity of temporal queries can be drastically reduced leading to more readable and less error-prone code without touching the database system. Our experiments show that the proposed operators significantly outperform temporal queries formulated in pure SQL. In addition, we investigate the possibility to incorporate algebraic optimization strategies directly into our operator definitions which allow for further performance improvements.

We introduce end-to-end support of co-existing schema versions within one database. While it is state of the art to run multiple versions of a continuously developed application concurrently, it is hard to do the same for databases. In order to keep multiple co-existing schema versions alive -- which are all accessing the same data set -- developers usually employ handwritten delta code (e.g. views and triggers in SQL). This delta code is hard to write and hard to maintain: if a database administrator decides to adapt the physical table schema, all handwritten delta code needs to be adapted as well, which is expensive and error-prone in practice. In this paper, we present InVerDa: developers use the simple bidirectional database evolution language BiDEL, which carries enough information to generate all delta code automatically. Without additional effort, new schema versions become immediately accessible and data changes in any version are visible in all schema versions at the same time. InVerDa also allows for easily changing the physical table design without affecting the availability of co-existing schema versions. This greatly increases robustness (orders of magnitude less lines of code) and allows for significant performance optimization. A main contribution is the formal evaluation that each schema version acts like a common full-fledged database schema independently of the chosen physical table design.

We investigate the possibility to use update propagation methods for optimizing the evaluation of continuous queries. Update propagation allows for the efficient determination of induced changes to derived relations resulting from an explicitly performed base table update. In order to simplify the computation process, we propose the propagation of updates with different degrees of granularity which corresponds to an incremental query evaluation with different levels of accuracy. We show how propagation rules for different update granularities can be systematically derived, combined and further optimized by using Magic Sets. This way, the costly evaluation of certain subqueries within a continuous query can be systematically circumvented allowing for cutting down on the number of pipelined tuples considerably.

The evaluation of continuous queries over data streams often becomes difficult as soon as static context data must be combined with dynamic stream data. This is especially the case if the context data is organized in form of view hierarchies and thus computed from some base facts. In this scenario, typical algebraic optimization strategies fail in providing a well-optimized query evaluation plan which effectively combines the stream and classical view subparts of the given query. The Magic Update method represents a possible solution to this problem as it allows for dynamically generating new selection conditions from the data stream which are pushed into the view hierarchy of context data. In this paper we present a case study in which the performance gain of this technique is shown when optimizing anomaly detection views in an air-traffic surveillance scenario.

In moving object databases, authors usually assume that number and position of objects to be processed are always known in advance. Detecting an unknown moving object and pursuing its movement, however, is usually left to tracking algorithms resting outside the database. Trackers are complex software systems which process sensor data and application-specific context information in order to detect, classify, monitor and predict the course of moving objects. As there are no universal software tools for realizing a tracker, such systems are usually hand-coded from scratch for each tracking application. In this paper we present a way how to implement a framework for implementing universal trackers inside a database. As a use case, we consider the well-known probabilistic multiple hypothesis tracking approach (PMHT) and the interacting multiple model filter (IMM) for realizing typical tracking tasks. We show that incremental view maintenance techniques and Bregman Ball trees are well-suited for efficiently implementing state-of-the-art trackers for processing streams of radar data.

Since navigation systems and tracking devices are becoming ubiquitous in our daily life, the development of efficient methods for processing massive sets of mobile objects are of utmost importance. Although future routes of mobile objects are often known in advance in many applications, this information is not fully utilized in most methods so far. In this paper, we reveal the beneficial effects of exploiting future routes for the early generation of the expected results of spatio-temporal queries. This kind of probable results is important for operative analytics in many applications like smart fleet management or intelligent logistics. For efficiently computing the high number of future trajectory points, a new index structure is presented which allows for a fast maintenance of query results under continuous changes of mobile objects. Our methods make use of specific update patterns, which require substantially less maintenance costs than the most general case of an update. A set of experiments based on a commonly used simulation environment shows the efficiency of our approach.

ETL jobs are used to integrate data from distributed and heterogeneous sources into a data warehouse. A well-known challenge in this context is the development of incremental ETL jobs for efficiently maintaining warehouse data in the presence of source data updates. In this paper, we present a new transformation-based approach to automatically derive incremental ETL jobs. To this end, we consider a simplification of the underlying update propagation process based on the computation of so-called safe updates instead of true ones. Additionally, we identify the limitations of already proposed incremental solutions, which are cured by employing Magic Sets leading to dramatic performance gains. 1.

This paper is concerned with a transformation-based approach to update propagation in an extended version of Codd's relational algebra which allows for defining derived relations (even recursively). It is shown that the desired optimization effects of update propagation may be lost if no generalized selection pushing strategy is employed to the transformed algebra expressions. A possible solution is the application of the Magic Sets rewriting but this may lead to unstratifiability of the incremental expressions. For the efficient evaluation of Magic Sets transformed algebra expressions we propose to use the soft stratification approach because of the simplicity and efficiency of this technique.

In this paper we show the usefulness and feasibility of applying conventional SQL queries for analyzing a wide spectrum of data streams. As application area we have chosen the analysis of stock market data, mainly because this kind of application exhibits sufficiently many of those characteristics for which relational query technology can be considered a valuable instrument in a stream context. The resulting TInTo system is a tool for computing so-called technical indicators, numerical values calculated from a certain kind of stock market data, characterizing the development of stock prices over a given time period. Update propagation is used for the incremental recomputation of indicator views defined over a stream of continuously changing price data.

In this paper we propose a new bottom-up query evaluation method for stratified deductive databases based on the Magic Set approach. As the Magic Sets rewriting may lead to unstratifiable rules, we propose to use Kerisit's weak consequence operator to compute the well-founded model of magic rules (guaranteed to be two-valued). We show that its application in combination with the concept weak stratification, however, may lead to a set of answers which is neither sound nor complete with respect to the well-founded model. This problem is cured by introducing the new concept soft stratification instead.