Parallel characterizations of a generalized Shapley value and a generalized Banzhaf value for cooperative games with level structure of cooperation

We present parallel characterizations of two different values in the framework of restricted cooperation games. The restrictions are introduced as a finite sequence of partitions defined on the player set, each of them being coarser than the previous one, hence forming a structure of different levels of a priori unions. On the one hand, we consider a value first introduced in Ref. [18], which extends the Shapley value to games with different levels of a priori unions. On the other hand, we introduce another solution for the same type of games, which extends the Banzhaf value in the same manner. We characterize these two values using logically comparable properties.     

Symbolic abstraction and deadlock-freeness verification of inter-enterprise processes

The design of complex inter-enterprise business processes (IEBP) is generally performed in a modular way. Each process is designed separately and then the whole IEBP is obtained by composition. Even if such a modular approach is intuitive and facilitates the design problem, it poses the problem that correct behavior of each business process of the IEBP taken alone does not guarantee a correct behavior of the composed IEBP (i.e. properties are not preserved by composition). Proving correctness of the (unknown) composed process is strongly related to the model checking problem of a system model. Among others, the symbolic observation graph based approach has proven to be very helpful for efficient model checking in general. Since it is heavily based on abstraction techniques and thus hides detailed information about system components that are not relevant for the correctness decision, it is promising to transfer this concept to the problem raised in this paper: How can the symbolic observation graph technique be adapted and employed for process composition? Answering this question is the aim of this paper.     

Reliable representations for association rules

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.     

Extending ?-diversity to generalize sensitive data

Generalization is an important technique for protecting privacy in data dissemination. In the framework of generalization, ?-diversity is a strong notion of privacy. However, since existing ?-diversity measures are defined in terms of the most specific (rather than general) sensitive attribute (SA) values, algorithms based on these measures can have narrow eligible ranges for data that has a heavily skewed distribution of SA values and produce anonymous data that has a low utility. In this paper, we propose a new ?-diversity measure called the functional (τ, ?)-diversity, which extends ?-diversity by using a simple function to constrain frequencies of base SA values that are induced by general SA values. As a result, algorithms based on (τ, ?)-diversity may generalize SA values, thus are much less constrained by skew SA distributions. We show that (τ, ?)-diversity is more flexible and elaborate than existing ?-diversity measures. We present an efficient heuristic algorithm that uses a novel order of quasi-identifier (QI) values to achieve (τ, ?)-diversity. We compare our algorithm with two state-of-the-art algorithms that are based on existing ?-diversity measures. Our preliminary experimental results indicate that our algorithm not only provides a stronger privacy protection but also results in better utility of anonymous data.     

Enhancing the semantics of UML association redefinition

Association redefinition is a UML construct that permits us to define an association end more specifically in a particular context. Concretely, it allows specifying some additional participation and cardinality constraints on the association. Association refinements, which have been studied and used by many authors in conceptual modelling languages prior to UML, are closely related to association redefinitions. They also permit to refine the ends of an association adding participation and cardinality constraints. In this paper, we analyze and compare the semantics of both concepts and propose to extend the semantics of association redefinitions in UML to cover all the constraints that may be expressed by association refinements in other conceptual modelling languages. Additionally, we present how to integrate previous results on validation of association refinements to UML and how to generate code for a relational technology platform. Finally, we provide a prototype tool to verify the feasibility of the approach.     

A regression-based approach for mining user movement patterns from random sample data

Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.     

An approximate duplicate elimination in RFID data streams

The RFID technology has been applied to a wide range of areas since it does not require contact in detecting RFID tags. However, due to the multiple readings in many cases in detecting an RFID tag and the deployment of multiple readers, RFID data contains many duplicates. Since RFID data is generated in a streaming fashion, it is difficult to remove duplicates in one pass with limited memory. We propose one pass approximate methods based on Bloom Filters using a small amount of memory. We first devise Time Bloom Filters as a simple extension to Bloom Filters. We then propose Time Interval Bloom Filters to reduce errors. Time Interval Bloom Filters need more space than Time Bloom Filters. We propose a method to reduce space for Time Interval Bloom Filters. Since Time Bloom Filters and Time Interval Bloom Filters are based on Bloom Filters, they do not produce false negative errors. Experimental results show that our approaches can effectively remove duplicates in RFID data streams in one pass with a small amount of memory.     

Document clustering using synthetic cluster prototypes

The use of centroids as prototypes for clustering text documents with the k-means family of methods is not always the best choice for representing text clusters due to the high dimensionality, sparsity, and low quality of text data. Especially for the cases where we seek clusters with small number of objects, the use of centroids may lead to poor solutions near the bad initial conditions. To overcome this problem, we propose the idea of synthetic cluster prototype that is computed by first selecting a subset of cluster objects (instances), then computing the representative of these objects and finally selecting important features. In this spirit, we introduce the MedoidKNN synthetic prototype that favors the representation of the dominant class in a cluster. These synthetic cluster prototypes are incorporated into the generic spherical k-means procedure leading to a robust clustering method called k-synthetic prototypes (k-sp). Comparative experimental evaluation demonstrates the robustness of the approach especially for small datasets and clusters overlapping in many dimensions and its superior performance against traditional and subspace clustering methods.