Peter Scheuermann

Querying and mining of time series data: experimental comparison of representations and distance measures

The last decade has witnessed a tremendous growths of interests in applications that deal with querying and mining of time series data. Numerous representation methods for dimensionality reduction and similarity measures geared towards time series have been introduced. Each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive set of time series experiments re-implementing 8 different representation methods and 9 similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this paper, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. Our experiments have provided both a unified validation of some of the existing achievements, and in some cases, suggested that certain claims in the literature may be unduly optimistic.

Experimental comparison of representation methods and distance measures for time series data

The previous decade has brought a remarkable increase of the interest in applications that deal with querying and mining of time series data. Many of the research efforts in this context have focused on introducing new representation methods for dimensionality reduction or novel similarity measures for the underlying data. In the vast majority of cases, each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive experimental study re-implementing eight different time series representations and nine similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this article, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. In addition to providing a unified validation of some of the existing achievements, our experiments also indicate that, in some cases, certain claims in the literature may be unduly optimistic.

Data partitioning and load balancing in parallel disk systems

Abstract. Parallel disk systems provide opportunities for exploiting I/O parallelism in two possible ways, namely via inter-request and intra-request parallelism. In this paper, we discuss the main issues in performance tuning of such systems, namely striping and load balancing, and show their relationship to response time and throughput. We outline the main components of an intelligent, self-reliant file system that aims to optimize striping by taking into account the requirements of the applications, and performs load balancing by judicious file allocation and dynamic redistributions of the data when access patterns change. Our system uses simple but effective heuristics that incur only little overhead. We present performance experiments based on synthetic workloads and real-life traces.

Proxy cache algorithms: design, implementation, and performance

Caching at proxy servers is one of the ways to reduce the response time perceived by World Wide Web users. Cache replacement algorithms play a central role in the response time reduction by selecting a subset of documents for caching, so that a given performance metric is maximized. At the same time, the cache must take extra steps to guarantee some form of consistency of the cached documents. Cache consistency algorithms enforce appropriate guarantees about the staleness of the cached documents. We describe a unified cache maintenance algorithm, LNC-R-WS-U, which integrates both cache replacement and consistency algorithms. The LNC-R-WS-U algorithm evicts documents from the cache based on the delay to fetch each document into the cache. Consequently, the documents that took a long time to fetch are preferentially kept in the cache. The LNC-R-W3-U algorithm also considers in the eviction consideration the validation rate of each document, as provided by the cache consistency component of LNC-R-WS-U. Consequently, documents that are infrequently updated and thus seldom require validations are preferentially retained in the cache. We describe the implementation of LNC-R-W3-U and its integration with the Apache 1.2.6 code base. Finally, we present a trace-driven experimental study of LNC-R-W3-U performance and its comparison with other previously published algorithms for cache maintenance.

Conceptual Modeling-ER 2003

The Semantic Web and the Web service paradigm are currently the most important trends on the way to the next generation of the Web. They promise new opportunities for content and service provision, enabling manifold and flexible new applications and improved support for individual and cooperative tasks. The use of the Web service paradigm in the development of Web applications, that typically couple application databases with user dialogs, is quite obvious. The development of Web applications that can be operated effectively in the Semantic Web context (Semantic Web Applications), however, imposes some challenges. Two main challenges towards extended (conceptual) modeling support are addressed in this talk: 1. In the Semantic Web, Web applications move from a purely human user community towards a mixed user community consisting of humans as well as of software agents; this results into new requirements towards models for Web applications’ user interfaces; 2. Automatic interpretation of content, one of the main building blocks of the Semantic Web, is based on interlinking local models with globally defined interpretation schemes like vocabularies and ontologies; this has to be reflected by the conceptual application domain models of Semantic Web Applications. Conceptual Modeling for Web applications, thus, has to be revisited in the context of the new Web trends looking for adequate Semantic Web Application Models. In Web applications dialog-oriented (in most cases form-based) user interface models are state-of-the art for the interaction with users. The requirement of representing interaction with humans as well as with software agents is best met by a user interface model that describes the dialogs with the system on a conceptual level that can be dynamically translated into a (user) interface language adequate for the respective “user” (human or agent). The upcoming Web standard XForms for the next generation of form-based user interfaces is a good example of such a conceptual user interface model. For the linking of globally defined concepts with local domain model concepts one of the most popular models in the context of the Semantic Web is provided by the Resource Description Framework (RDF). The systematic integration of Uniform Resource Identifiers (URIs) into the model facilitates references to vocabularies and ontologies defined e.g. as RDF Schema or OWL ontology. However, for using RDF in Web applications a coupling between these “semantic” data models and the more traditional data models underlying the application data is necessary. I.-Y. Song et al. (Eds.): ER 2003, LNCS 2813, pp. 1–2, 2003. c

File assignment in parallel I/O systems with minimal variance of service time

We address the problem of assigning nonpartitioned files in a parallel I/O system where the file accesses exhibit Poisson arrival rates and fixed service times. We present two new file assignment algorithms based on open queuing networks which aim at minimizing simultaneously the load balance across all disks, as well as the variance of the service time at each disk. We first present an off-line algorithm, Sort Partition, which assigns to each disk file with similar access time. Next, we show that, assuming that a perfectly balanced file assignment can be found for a given set of files, Sort Partition will find the one with minimal mean response time. We then present an on-line algorithm, Hybrid Partition, that assigns groups of files with similar service times in successive intervals while guaranteeing that the load imbalance at any point does not exceed a certain threshold. We report on synthetic experiments which exhibit skew in file accesses and sizes and we compare the performance of our new algorithms with the vanilla greedy file allocation algorithm.

A case for delay-conscious caching of Web documents

Caching at proxy servers plays an important role in reducing the latency of the user response, the network delays and the load on Web servers. The cache performance depends critically on the design of the cache replacement algorithm. Unfortunately, most cache replacement algorithms ignore the Webs scale. In this paper we argue for the design of delay-conscious cache replacement algorithms which explicitly consider the Webs scale by preferentially caching documents which require a long time to fetch to the cache. We present a new, delay-conscious cache replacement algorithm LNC-R-W3 which maximizes a performance metric called delay-savings-ratio. Subsequently, we test the performance of LNC-R-W3 experimentally and compare it with the performance of other existing cache replacement algorithms, namely LRU and LRU-MIN.

Probabilistic range queries for uncertain trajectories on road networks

Trajectories representing the motion of moving objects are typically obtained via location sampling, e.g. using GPS or road-side sensors, at discrete time-instants. In-between consecutive samples, nothing is known about the whereabouts of a given moving object. Various models have been proposed (e.g., sheared cylinders; spacetime prisms) to represent the uncertainty of the moving objects both in unconstrained Euclidian space, as well as road networks. In this paper, we focus on representing the uncertainty of the objects moving along road networks as time-dependent probability distribution functions, assuming availability of a maximal speed on each road segment. For these settings, we introduce a novel indexing mechanism -- UTH (Uncertain Trajectories Hierarchy), based upon which efficient algorithms for processing spatio-temporal range queries are proposed. We also present experimental results that demonstrate the benefits of our proposed methodologies.

Continuous probabilistic nearest-neighbor queries for uncertain trajectories

This work addresses the problem of processing continuous nearest neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing.

Multidimensional B-trees for associative searching in database systems

Abstract A new method for multiple attribute indexing, the Multidimensional B -Tree (MBDT), is developed. This method is well suited for dynamic databases, since it handles several types of associative queries efficiently and requires low-cost maintenance. Algorithms and search strategies for exact match, partial match, and range queries are presented and statistical procedures are given to estimate the average and worst case retrieval times. The applicability of our organization to practical databases is discussed and analytical tradeoffs with regard to index organizations based on k-d trees are established.