Location Based Logistics Services and Event Driven Business Process Management

Location-based Services (LBS) [1] have already started their market penetration process and several platforms capable of running LBS are available. With the number of LBS increasing, the current development is targeting consumer applications, although LBS have a high potential for enhancing business processes in companies as well. Considering business process optimization, one concept recently discussed in the field of Business Process Management (BPM) [2] is Event-Driven Business Process Management (ED-BPM) [3], which combines Business Process Management (BPM) and Complex Event Processing (CEP) [4]. This paper introduces ED-BPM for LBS in the logistics field, exemplifying its potential use with an example for a logistics order process execution

Mixed type audio classification with support vector machine

Content-based classification of audio data is an important problem for various applications such as overall analysis of audio-visual streams, boundary detection of video story segment, extraction of speech segments from video, and content-based video retrieval. Though the classification of audio into single type such as music, speech, environmental sound and silence is well studied, classification of mixed type audio data, such as clips having speech with music as background, is still considered a difficult problem. In this paper, we present a mixed type audio classification system based on Support Vector Machine (SVM). In order to capture characteristics of different types of audio data, besides selecting audio features, we also design four different representation formats for each feature. Our SVM-based audio classifier can classify audio data into five types: music, speech, environment sound, speech mixed with music, and music mixed with environment sound. The experimental results show that our system outperforms other classification systems using k Nearest Neighbor (k-NN), Neural Network (NN), and Naive Bayes (NB). © 2006 IEEE

T.: Scalable Splitting of Massive Data Streams

Abstract. Scalable execution of continuous queries over massive data streams often requires splitting input streams into parallel sub-streams over which query operators are executed in parallel. Automatic stream splitting is in general very difficult, as the optimal parallelization may depend on application semantics. To enable application specific stream splitting, we introduce splitstream functions where the user specifies non-procedural stream partitioning and replication. For high-volume streams, the stream splitting itself becomes a performance bottleneck. A cost model is introduced that estimates the performance of splitstream functions with respect to throughput and CPU usage. We implement parallel splitstream functions, and relate experimental results to cost model estimates. Based on the results, a splitstream function called autosplit is proposed, which scales well for high degrees of parallelism, and is robust for varying proportions of stream partitioning and replication. We show how user defined parallelization using autosplit provides substantially improved scalability (L = 64) over previously published results for the Linear Road Benchmark

Business process modeling notation

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Process mining

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Composition

No abstract