Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining...
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Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis techniques. Recently, process mining techniques have been adopted in various commercial BPM systems (e.g., BPM vertical bar one, Futura Reflect, ARIS PPM, Fujitsu Interstage, Businesscape, Iontas PDF, and QPR PA). Unfortunately, traditional process discovery algorithms have problems dealing with less structured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in such a way that event logs can be explored easily. Trace alignment can be used to explore the process in the early stages of analysis and to answer specific questions in later stages of analysis. Hence, it complements existing process mining techniques focusing on discovery and conformance checking. The proposed techniques have been implemented as plugins in the ProM framework. We report the results of trace alignment on one synthetic and two real-life event logs, and show that trace alignment has significant promise in process diagnostic efforts. (C) 2011 Elsevier Ltd. All rights reserved.
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of pro...
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Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of programming abstractions designed for optimizing GNN Aggregation have emerged to facilitate acceleration. However, there is no comprehensive evaluation and analysis upon existing abstractions, thus no clear consensus on which approach is better. In this letter, we classify existing programming abstractions for GNN Aggregation by the dimension of data organization and propagation method. By constructing these abstractions on a state-of-the-art GNN library, we perform a thorough and detailed characterization study to compare their performance and efficiency, and provide several insights on future GNN acceleration based on our analysis.
Program comprehension is a major challenge for system maintenance. Reverse engineering has been employed for control-flow analysis of applications but not much work has been done for comprehending concurrent non-deter...
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ISBN:
(纸本)9781509041275
Program comprehension is a major challenge for system maintenance. Reverse engineering has been employed for control-flow analysis of applications but not much work has been done for comprehending concurrent non-deterministic behavior of multi-threaded applications. We present D-CUBE, built using dynamic instrumentation APIs, which plugs in during execution and infers various thread models like concurrency, safety, data access, thread-pool state, exception model etc. for multi-threaded applications at runtime. We extract run-time events traced according to pre-specified logic and feed them to decision trees for inference. We use 3 benchmark suites (LOC: 50-3200) - CDAC Pthreads benchmark [1] (18 Cases), Open POSIX Test-Suites [2] (21 Cases) and PARSEC 3.0 benchmarks [3] (3 Cases) for accuracy and volume testing and validate our approach by comparing the documented behavior of test-suites with D-CUBE's output models. We achieve over 90% accuracy. D-CUBE produces graphical event-traces with every inference for quick and effective comprehension of large code.
Nowadays we are witnessing the fast spreading of smart grid technology deployments. An increase of smart grid services and applications is also expected. Therefore in our work we aim to propose and develop user applic...
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ISBN:
(纸本)9781457717710
Nowadays we are witnessing the fast spreading of smart grid technology deployments. An increase of smart grid services and applications is also expected. Therefore in our work we aim to propose and develop user applications on top of smart grid power infrastructures that monitor, analyze, classify and characterize different electronic devices connected to this infrastructure. The present paper aims to propose a solution for ideal power signature extraction for consumer devices. Power signature may deeply characterize the electronic devices functioning. This signature can be used to identify energy efficiency usage patterns and provide feedback to users in order to reduce energy consumption and increase the lifetime of the products. Power signature of an electronic device is defined as the power consumption response to certain workload or program executed by the device.
The continuous proliferation of multicore architectures has placed developers under great pressure to parallelize their applications accordingly with what such platforms can offer. Unfortunately, traditional low-level...
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ISBN:
(纸本)9780769547497
The continuous proliferation of multicore architectures has placed developers under great pressure to parallelize their applications accordingly with what such platforms can offer. Unfortunately, traditional low-level programming models exacerbate the difficulties of building large and complex parallel applications. High-level parallel programming models are in high-demand as they reduce the burdens on programmers significantly and provide enough abstraction to accommodate hardware heterogeneity. In this paper, we propose a flexible parallelization methodology, and we introduce a new task-based hybrid programming model (MHPM) designed to provide high productivity and expressiveness without sacrificing performance. We show that MHPM allows easy expression of both sequential execution and several types of parallelism including task, data and temporal parallelism at all levels of granularity inside a single structured homogeneous programming model. In order to demonstrate the potential of our approach, we present a pure C++ implementation of MHPM, and we show that, despite its high abstraction, it provides comparable performances to lower-level programming models.
Process mining techniques can be used to extract non-trivial process-related knowledge and thus generate interesting insights from event logs. Similarly, bioinformatics aims at increasing the understanding of biologic...
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ISBN:
(纸本)9783642297489
Process mining techniques can be used to extract non-trivial process-related knowledge and thus generate interesting insights from event logs. Similarly, bioinformatics aims at increasing the understanding of biological processes through the analysis of information associated with biological molecules. Techniques developed in both disciplines can benefit from one another, e.g., sequence analysis is a fundamental aspect in both process mining and bioinformatics. In this paper, we draw a parallel between bioinformatics and process mining. In particular, we present some initial success stories that demonstrate that the emerging process mining discipline can benefit from techniques developed for bioinformatics.
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