Abstract not found

Temporal aggregation is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). In this paper we examine the problem of computing temporal aggregates over data streams. Such aggregates are maintained using multiple levels of temporal granularities: older data is aggregated using coarser granularities while more recent data is aggregated with finer detail. We present specialized indexing schemes for dynamically and progressively maintaining temporal aggregates.

Temporal and Spatio-Temporal Aggregations are... In this paper we examine the problem of computing such aggregates over data streams. The aggregates are maintained using multiple levels of temporal granularities: older data is aggregated using coarser granularities while more recent data is aggregated with finer detail. We present specialized indexing schemes for dynamically and progressively maintaining temporal and spatio-temporal aggregates. Moreover, these schemes can be parameterized. The levels of granularity as well as their corresponding index sizes (or validity lengths) can be dynamically adjusted. This provides a useful trade-off between aggregation detail and storage space. Analytical and experimental results show the efficiency of the proposed structures. We first address the temporal aggregation problem. A general framework of aggregating at multiple time granularities is then proposed. Finally we show how to utilize this framework to solve the range temporal and spatio-temporal aggregation problems.

The analysis of the semantics of temporal data and queries plays a central role in the area of temporal databases. Although many different algebræ and models have been proposed, almost all of them are based on a point-based (snapshot) semantics for data. On the other hand, in the areas of linguistics, philosophy, and, recently, artificial intelligence, an oft-debated issue concerns the use of an interval-based versus a point-based semantics. In this paper, we first show some problems inherent in the adoption of a point-based semantics for data, then argue that these problems arise because there is no distinction drawn in the data between telic and atelic facts. We then introduce a three-sorted temporal model and algebra including coercion functions for transforming relations of one sort into relations of the other at query time which properly copes with these issues.

A well-known problem of temporal databases is that the amount of stored data tends to increase very fast. Moreover, detailed data are useful when they are acquired but they often become less relevant after some time. In most cases, after a period of time only summarized data need to be kept, whereas detailed data expire and can be removed from the database. Multigranular temporal databases enhance the expressive power of temporal databases by supporting temporal attributes at different levels of detail. However, in existing approaches the level of detail of an attribute, that is its granularity, depends only on the attribute semantics and does not depend on how recent the attribute values are. This paper proposes an approach supporting the aggregation of different portions of the value of a temporal attribute at different levels of detail, and the deletion or the transfer to tertiary storage of old values at a given level of detail, in order to minimize disk storage occupancy. In the proposed multigranular temporal object-oriented data model, the expiration of attribute values at a given granularity can be specified, together with the action to take when data expire: either aggregation to a coarser granularity, or deletion of values, or both. Keywords: Temporal databases, multiple granularities, granularity evolution, deletion of temporal values, expiration of dynamic attributes.

Abstract. In the area of Medical Informatics, there is an increasing realization that temporal information plays a crucial role, so that suitable database models and query languages are needed to store and support it. In this paper we show that current approaches developed within the database field have some limitations even from the point of view of the data model, so that an important class of temporal medical data cannot be properly represented. We propose a new three-sorted model and a query language that overcome such limitations. 1

Abstract. A significant property of temporal data is their richness of semantics. Although several temporal data models and query languages have been designed specifically to handle the temporal data, users must still deal with much of the implicit temporal information, which can be automatically derived from the stored data in certain situations. We propose a multidatabase architecture where an appropriate formalization of the intended semantics is associated with each temporal relation and temporal database. This allows a temporal mediator to access the databases to retrieve implicit information in terms of time granularities different from those used to store data. We also describe how the temporal mediator can provide a user interface to the multidatabase system allowing temporal queries in terms of arbitrary granularities and involving relations in different TDBMS. 1

The aggregation query is an important but costly operation in database management systems. While the aggregation in relational databases has been well studied, recently there has been a growing interest in improving the performance of computing aggregates over complex objects. Each of such objects may have a time interval, a spatial location/region, or both, as appears in temporal, spatial, and spatio-temporal databases. An aggregation query over these objects typically involves some selection condition on their time and/or spatial attributes, e.g. to aggregate over temporal records whose time intervals intersect a given time interval. A straightforward approach is to, with the aid of some index structures, locate the objects that satisfy the selection condition and aggregate their values on the fly. Such indices are general in the sense that they can be utilized not only to compute aggregates, but also to perform selection queries. However, the aggregation query performance is proportional to the number of objects satisfying the selection condition. In the worst case, to comv pute an aggregate, all objects in a database needs to be examined. Nevertheless, in many applications (on-line analysis, etc.), we need to compute the aggregates very fast, and to scan through all the existing objects might be too time-consuming. In this thesis, we focus on devising specialized indices for aggregation over complex objects. Our research shows that our newly designed structures have much better query performance than the existing general-index-based solutions, sometimes over a hundred times faster. In...

Many events repeat themselves as the time goes by. For example, an institute pays its employees on the first day of every month.

Because it is difficult to engineer a complex ontology with time, we here consider a method that allows for factorizing the complexity of the engineering process, FONTE (Factorizing ONTology Engineering complexity). FONTE divides the engineering task into building a time-less domain ontology and a temporal theory independently from each other. FONTE provides an operator that assembles the two independently developed ontologies into the targeted ontology. We investigate the quality of the proposed operator by applying it to a practical case study, viz. the engineering of an ontology about researchers including temporal interactions.