Abstract not found

Abstract. Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture when the data hold. In temporal databases, intervals typically capture the states of reality that the data apply to, or capture when the data are, or were, part of the current database state. This paper proposes a new aggregation operator that addresses several challenges posed by interval data. First, the intervals to be associated with the result tuples may not be known in advance, but depend on the actual data. Such unknown intervals are accommodated by allowing result groups that are specified only partially. Second, the operator contends with the case where an interval associated with data expresses that the data holds for each point in the interval, as well as the case where the data holds only for the entire interval, but must be adjusted to apply to sub-intervals. The paper reports on an implementation of the new operator and on an empirical study that indicates that the operator scales to large data sets and is competitive with respect to other temporal aggregation algorithms. 1

For the last years, time-series mining has become a challenging issue for researchers. An important application lies in most monitoring purposes, which require analyzing large sets of time-series for learning usual patterns. Any deviation from this learned profile is then considered as an unexpected situation. Moreover, complex applications may involve the temporal study of several heterogeneous parameters. In that paper, we propose a method for mining heterogeneous multivariate time-series for learning meaningful patterns. The proposed approach allows for mixed time-series – containing both pattern and non-pattern data – such as for imprecise matches, outliers, stretching and global translating of patterns instances in time. We present the early results of our approach in the context of monitoring the health status of a person at home. The purpose is to build a behavioral profile of a person by analyzing the time variations of several quantitative or qualitative parameters recorded through a provision of sensors installed in the home.

Real-world data management applications generally manage temporal data, i.e., they manage multiple states of time-varying data. Many contributions have been made by the research community for how to better model, store, and query temporal data. In particular, several dozen temporal data models and query languages have been proposed. Motivated in part by the emergence of non-traditional data management applications and the increasing proliferation of temporal data, this paper puts focus on the aggregation of temporal data. In particular, it provides a general framework of temporal aggregation concepts, and it discusses the abilities of five approaches to the design of temporal query languages with respect to temporal aggregation. Rather than providing focused, polished results, the paper’s aim is to explore the inherent support for temporal aggregation in an informal manner that may serve as a foundation for further exploration. 1

This paper studies aggregate search in transaction time databases. Specifically, each object in such a database can be modeled as a horizontal segment, whose y-projection is its search key, and its x-projection represents the period when the key was valid in history. Given a query timestamp qt and a key range �qk, a count-query retrieves the number of objects that are alive at qt, and their keys fall in �qk. We provide a method that accurately answers such queries, with error less than 1 ε + ε · Nalive(qt), where Nalive(qt) is the number of objects alive at time qt, and ε is any constant in (0,1]. Denoting the disk page size as B, and n = N/B, our technique requires O(n) space, processes any query in O(logB n) time, and supports each update in O(logB n) amortized I/Os. As demonstrated by extensive experiments, the proposed solutions guarantee query results with extremely high precision (median relative error below 5%), while consuming only a fraction of the space occupied by the existing approaches that promise precise results. To appear in VLDB Journal.

Computer Science Geospatial image data obtained by satellites and aircraft are increasingly important to a wide range of applications, such as disaster management, climatol-ogy, and environmental monitoring. Spatio-temporal aggregations are some of the most important operations over such data. Because of the size of the data and the speed at which it is generated, computing such aggregates over geospatial image data is extremely demanding. Due to the special characteristics of the data, existing spatio-temporal aggregation models and evaluation approaches are not suitable for computing aggregates over such data. In this thesis, we analyze the characteristics of streaming geospatial image data and outline the key challenges of spatio-temporal aggregate computations. By showing that traditional aggregation models do not always provide an accurate view of the data, we propose new spatio-temporal aggregation models that infuse a more meaningful semantics into a query. More importantly, our experiments show that ex-isting approaches do not evaluate these queries efficiently. Existing approaches do not

Temporal aggregation is a crucial operator in temporal databases and has been studied in various flavors. In instant temporal aggregation (ITA) the aggregate value at time instant t is computed from the tuples that hold at t. ITA considers the distribution of the input data and works at the smallest time granularity, but the result size depends on the input timestamps and can get twice as large as the input relation. In span temporal aggregation (STA) the user specifies the timestamps over which the aggregates are computed and thus controls the result size. In this paper we introduce a new temporal aggregation operator, called greedy parsimonious temporal aggregation (PTAg), which combines features from ITA and STA. The operator extends and approximates ITA by greedily merging adjacent tuples with similar aggregate values until the number of result tuples is sufficiently small, which can be controlled by the application. Thus, PTAg considers the distribution of the data and allows to control the result size. Our empirical evaluation on real world data shows good results: considerable reductions of the result size introduce small errors only. 1

Temporal aggregation is a crucial operator in temporal databases and has been studied in various flavors, including instant temporal aggregation (ITA) and span temporal aggregation (STA), each having its strengths and weaknesses. In this paper we define a new temporal aggregation operator, called parsimonious temporal aggregation (PTA), which comprises two main steps: (i) it computes the ITA result over the input relation and (ii) it compresses this intermediate result to a user-specified size c by merging adjacent tuples and keeping the induced total error minimal; the compressed ITA result is returned as the final result. By considering the distribution of the input data and allowing to control the result size, PTA combines the best features of ITA and STA. We provide two evaluation algorithms for PTA queries. First, the oPTA algorithm computes an exact solution, by applying dynamic programming to explore all possibilities to compress the ITA result and selecting the compression with the minimal total error. It runs in O(n 2 pc) time and O(n 2) space, where n is the size of the input relation and p is the number of aggregation functions in the query. Second, the more efficient gPTA algorithm computes an approximate solution by greedily merging the most similar ITA result tuples, which, however, does not guarantee a compression with a minimal total error. gPTA intermingles the two steps of PTA and avoids large intermediate results. The compression step of gPTA runs in O(np log(c + δ)) time and O(c+δ) space, where δ is a small buffer for “look ahead”. An empirical evaluation shows good results: considerable reductions of the result size introduce only small errors, and gPTA scales to large data sets and is only slightly worse than the exact solution of PTA. 1.