Chaokun Wang

A workflow net similarity measure based on transition adjacency relations

Many activities in business process management, such as process retrieval, process mining, and process integration, need to determine the similarity or the distance between two processes. Although several approaches have recently been proposed to measure the similarity between business processes, neither the definitions of the similarity notion between processes nor the measure methods have gained wide recognition. In this paper, we define the similarity and the distance based on firing sequences in the context of workflow nets (WF-nets) as the unified reference concepts. However, to many WF-nets, either the number of full firing sequences or the length of a single firing sequence is infinite. Since transition adjacency relations (TARs) can be seen as the genes of the firing sequences which describe transition orders appearing in all possible firing sequences, we propose a practical similarity definition based on the TAR sets of two processes. It is formally shown that the corresponding distance measure between processes is a metric. An algorithm using model reduction techniques for the efficient computation of the measure is also presented. Experimental results involving comparison of different measures on artificial processes and evaluations on clustering real-life processes validate our approach.

Community detection in social networks: an in-depth benchmarking study with a procedure-oriented framework

Revealing the latent community structure, which is crucial to understanding the features of networks, is an important problem in network and graph analysis. During the last decade, many approaches have been proposed to solve this challenging problem in diverse ways, i.e. different measures or data structures. Unfortunately, experimental reports on existing techniques fell short in validity and integrity since many comparisons were not based on a unified code base or merely discussed in theory. We engage in an in-depth benchmarking study of community detection in social networks. We formulate a generalized community detection procedure and propose a procedure-oriented framework for benchmarking. This framework enables us to evaluate and compare various approaches to community detection systematically and thoroughly under identical experimental conditions. Upon that we can analyze and diagnose the inherent defect of existing approaches deeply, and further make effective improvements correspondingly. We have re-implemented ten state-of-the-art representative algorithms upon this framework and make comprehensive evaluations of multiple aspects, including the efficiency evaluation, performance evaluations, sensitivity evaluations, etc. We discuss their merits and faults in depth, and draw a set of take-away interesting conclusions. In addition, we present how we can make diagnoses for these algorithms resulting in significant improvements.

Phosphor: A Cloud Based DRM Scheme with Sim Card

As 3G networks provide enhanced capabilities of data transportation, a considerable amount of mobile applications and services, which involve mass of unstructured digital content, e.g., video, audio, are available. Meanwhile, pirate and illegal distribution of these digital contents are severe issues. Digital Rights Management (DRM) aims at protecting unstructured digital contents from being abused through regulating the usage of digital contents. However, existing mobile DRM schemes more or less suffer from poor compatibility, bad practicality or high cost. Besides, to the best of our knowledge, fewer of the existent DRM schemes concern for efficient unstructured data management. In this paper, we propose Phosphor, a cloud based mobile DRM scheme with sim card. In details, an unstructured data management system is adopted for efficient data management services in DRM backend (servers and systems). Meanwhile, sim card is introduced to Phosphor, which not only reduces the cost, but also provides higher security. We have implemented our DRM scheme, which demonstrates that Phosphor is efficient, secure and practicable.

Follow But No Track: Privacy Preserved Profile Publishing in Cyber-Physical Social Systems

Due to the close correlation with individual’s physical features and status, the adoption of cyber-physical social systems (CPSSs) has been inevitably hindered by users’ privacy concerns. Such concerns keep growing as our bile devices have more embedded sensors, while the existing countermeasures only provide incapable and limited privacy preservation for sensitive physical information. Therefore, we propose a novel privacy preservation framework for CPSSs. We formulate both the privacy concerns and user expectations in CPSSs based on real-world knowledge. We also design a corresponding data publishing mechanism for users. It regulates the publishing behaviors to hide sensitive physical profiles. Meanwhile, the published data retain comprehensive social profiles for users. Our analysis demonstrates that the mechanism achieves a local maximized performance on the aspect published data size. The experiment results toward real datasets reveals that the performance is comparable to the global optimal one.

An Advanced MapReduce: Cloud MapReduce, Enhancements and Applications

Recently, Cloud Computing is attracting great attention due to its provision of configurable computing resources. MapReduce (MR) is a popular framework for data-intensive distributed computing of batch jobs. MapReduce suffers from the following drawbacks: 1. It is sequential in its processing of Map and Reduce Phases 2. Being cluster based, its scalability is relatively limited. 3. It does not support flexible pricing. 4. It does not support stream data processing. We describe Cloud MapReduce (CMR), which overcomes these limitations. Our results show that CMR is more efficient and runs faster than other implementations of the MR framework. In addition to this, we showcase how CMR can be further enhanced to: 1. Support stream data processing in addition to batch data by parallelizing the Map and Reduce phases through a pipelining model. 2. Support flexible pricing using Amazon Clouds spot instances and to deal with massive machine terminations caused by spot price fluctuations. 3. Improve throughput and speed-up processing over traditional MR by more than 30% for large data sets. 4. Provide added flexibility and scalability by leveraging features of the cloud computing model. Click-stream analysis, real-time multimedia processing, time-sensitive analysis and other stream processing applications can also be supported.

Approximate Holistic Aggregation in Wireless Sensor Networks

Holistic aggregation results are important for users to obtain summary information from Wireless Sensor Networks (WSNs). Holistic aggregation requires all the sensory data to be sent to the sink, which costs a huge amount of energy. Fortunately, in most applications, approximate results are acceptable. We study the approximated holistic aggregation algorithms based on uniform sampling. In this paper, four holistic aggregation operations are investigated. The mathematical methods to construct their estimators and determine the optional sample size are proposed, and the correctness of these methods is proved. Four corresponding distributed holistic algorithms are presented. The theoretical analysis and simulation results show that the algorithms have high performance.

A novel watermarking method for software protection in the cloud

With the rapid development of cloud computing, software applications are shifting onto cloud storage rather than remaining within local networks. Software distributions within the cloud are subject to security breaches, privacy abuses, and access control violations. In this paper, we identify an insider threat to access control which is not completely eliminated by the usual techniques of encryption, cryptographic hashes, and access‐control labels. We address this threat using software watermarking. We evaluate our access‐control scheme within the context of a Collaboration‐oriented Architecture, as defined by The Jericho Forum. Copyright

An Optimal Approach for Workflow Staff Assignment Based on Hidden Markov Models

Staff assignment of workflow is often performed manually and empirically. In this paper we propose an optimal approach named SAHMM ( Staff Assignment based on Hidden Markov Models ) to allocate the most proficient set of employees for a whole business process based on workflow event logs. The Hidden Markov Model( HMM ) is used to describe the complicated relationships among employees which are ignored by previous approaches. The validity of the approach is confirmed by experiments on real data.

A Watermark-Aware Trusted Running Environment for Software Clouds

A software cloud is ready-made for software delivery or Software as a Service (SaaS). As it becomes more and more popular, security problems of software running in the cloud also become an important issue. The threats, such as destroying system softwares and access violation, are frequently emerging in the field of software cloud. In this paper, we propose a watermark-aware trusted running environment to protect the softwares running in the cloud. We implement the scheme which mainly contains two parts: 1) embedding watermark into the Java programs running in the cloud; 2) generating customized JVMs for recognizing the watermarked programs. Experimental results within a real private software cloud to demonstrate that our approach can provide a large-scale protection with a small overhead.

ATBaM: An Arnold Transform Based Method on Watermarking Relational Data

ATBaM, A novel numeric relational data watermarking method, is presented in this paper. ATBaM substantially improves watermark security by using image scrambling technology which could confuse the well-regulated watermarking information and diffuse errors. Theoretical analysis proves the correctness and security of ATBaM. Also, experimental results show the strong robustness of ATBaM.