Sen Su

A novel genetic algorithm for qos-aware web services selection

A novel genetic algorithm characterized by improved fitness value is presented for Quality of Service (QoS)-aware web services selection. The genetic algorithm includes a special relation matrix coding scheme of chromosomes, an initial population policy and a mutation policy. The relation matrix coding scheme suits with QoS-aware web service composition more than the one dimension coding scheme. By running only once, the proposed genetic algorithm can construct the composite service plan according with the QoS requirement from many services compositions. Meanwhile, the adoption of the initial population policy and the mutation policy promotes the fitness of genetic algorithm. Experiments on QoS-aware web services selection show that the genetic algorithm with this matrix can get more excellent composite service plan than the genetic algorithm with the one dimension coding scheme, and that the two policies play an important role at the improvement of the fitness of genetic algorithm.

Efficient population diversity handling genetic algorithm for qos-aware web services selection

To maximize user satisfaction during composition of web services, a genetic algorithm with population diversity handling is presented for Quality of Service(QoS)-aware web services selection. In this algorithm, the fitness function, the selection mechanism of the population as well as the competition mechanism of the population are represented. The population diversity and population fitness are used as the primary criteria of the population evolution. By competing between the current population and the historical optimal population, the whole population evolution can be done on the basis of the whole population evolution principle of the biologic genetic theory. Prematurity is overcome effectively. Experiments on QoS-aware web services selection show that the genetic algorithm with population diversity handling can get more excellent composite service plan than the standard genetic algorithm.

A peer-to-peer approach to semantic web services discovery

Web Services with distributed and dynamic characteristics need efficient and decentralized discovery infrastructure supporting the semantic descriptions and discovery. In this paper, a novel peer-to-peer indexing system with related P2P registries is proposed to support the completely decentralized discovery capabilities. In the presented system, with the ontology encoding scheme, the semantic service description is distributed into a distributed trie index on the structured P2P network to allow requesters to lookup services with semantic requirements. Finally, experimental result shows that the presented system is efficient and scalable.

Detecting Race Conditions in Web Services

Feature interaction has been first formally identified as a problem in the telecommunication domain. With the rapid growth and application of Web Services, feature interaction problem also arises in the dynamic Web Service interactions. Race conditions, one type of functional feature interactions in Web Services, will lead to the unexpected system behavior in the composite Web Services system. This paper proposed a Petri net-based method to detect the race conditions in Web Services. The method can be divided into two phases: modeling Web Services and detecting the race conditions. On the basis of modeling Web Services, the Petri net analysis methods are employed in the detection algorithm. This static detection method will help avoid race conditions in the composite Web Services.

Using case-based reasoning to support web service composition

With the growing number of Web service, it is necessary to implement web service composition automatically. This paper presents an approach to support large-granularity web service composition accurately and fast according to users’ requests. With this approach, it can reduce the cost of web service composition, and improve scalability, reusability and efficiency. Using a proactive well defined service base, web service composition execution engine can gain the logic with Case-Based Reasoning technology. Comparing to other method and qualitative analysis, the approach proposed by this paper can solve the problem of web service composition under the condition of insufficient and ill-defined knowledge, and can reduce the difficulty and cost of web service composition.

Differentially private frequent sequence mining via sampling-based candidate pruning

In this paper, we study the problem of mining frequent sequences under the rigorous differential privacy model. We explore the possibility of designing a differentially private frequent sequence mining (FSM) algorithm which can achieve both high data utility and a high degree of privacy. We found, in differentially private FSM, the amount of required noise is proportionate to the number of candidate sequences. If we could effectively reduce the number of unpromising candidate sequences, the utility and privacy tradeoff can be significantly improved. To this end, by leveraging a sampling-based candidate pruning technique, we propose a novel differentially private FSM algorithm, which is referred to as PFS2. The core of our algorithm is to utilize sample databases to further prune the candidate sequences generated based on the downward closure property. In particular, we use the noisy local support of candidate sequences in the sample databases to estimate which sequences are potentially frequent. To improve the accuracy of such private estimations, a sequence shrinking method is proposed to enforce the length constraint on the sample databases. Moreover, to decrease the probability of misestimating frequent sequences as infrequent, a threshold relaxation method is proposed to relax the user-specified threshold for the sample databases. Through formal privacy analysis, we show that our PFS2 algorithm is ϵ-differentially private. Extensive experiments on real datasets illustrate that our PFS2 algorithm can privately find frequent sequences with high accuracy.

DP-Apriori

In this paper, we study the problem of designing a differentially private FIM algorithm which can simultaneously provide a high level of data utility and a high level of data privacy. This task is very challenging due to the possibility of long transactions. A potential solution is to limit the cardinality of transactions by truncating long transactions. However, such approach might cause too much information loss and result in poor performance. To limit the cardinality of transactions while reducing the information loss, we argue that long transactions should be split rather than truncated. To this end, we propose a transaction splitting based differentially private FIM algorithm, which is referred to as DP-Apriori. In particular, a smart weighted splitting technique is proposed to divide long transactions into sub-transactions whose cardinality is no more than a specified number of items. In addition, to offset the information loss caused by transaction splitting, a support estimation technique is devised to estimate the actual support of itemsets in the original database. Through privacy analysis, we show that our DP-Apriori algorithm is ? -differentially private. Extensive experiments on real-world datasets illustrate that DP-Apriori substantially outperforms the state-of-the-art techniques.

Differentially private frequent subgraph mining

Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individuals privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies ε-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility.

Co-ClusterD: A Distributed Framework for Data Co-Clustering with Sequential Updates

Co-clustering has emerged to be a powerful data mining tool for two-dimensional co-occurrence and dyadic data. However, co-clustering algorithms often require significant computational resources and have been dismissed as impractical for large data sets. Existing studies have provided strong empirical evidence that expectation-maximization (EM) algorithms (e.g., k-means algorithm) with sequential updates can significantly reduce the computational cost without degrading the resulting solution. Motivated by this observation, we introduce sequential updates for alternate minimization co-clustering (AMCC) algorithms which are variants of EM algorithms, and also show that AMCC algorithms with sequential updates converge. We then propose two approaches to parallelize AMCC algorithms with sequential updates in a distributed environment. Both approaches are proved to maintain the convergence properties of AMCC algorithms. Based on these two approaches, we present a new distributed framework, Co-ClusterD, which supports efficient implementations of AMCC algorithms with sequential updates. We design and implement Co-ClusterD, and show its efficiency through two AMCC algorithms: fast nonnegative matrix tri-factorization (FNMTF) and information theoretic co-clustering (ITCC). We evaluate our framework on both a local cluster of machines and the Amazon EC2 cloud. Empirical results show that AMCC algorithms implemented in Co-ClusterD can achieve a much faster convergence and often obtain better results than their traditional concurrent counterparts.

Evaluating proposals in web services negotiation

Negotiation is a crucial stage of Web Services interaction lifecycle. By exchanging a sequence of proposals in the negotiation stage, a service provider and a consumer try to establish a formal contract, to specify agreed terms on the service, particularly terms on non-functional aspects. In this paper, we propose an approach for proposal evaluation, one of the key decisions in negotiation that determines the acceptability of a proposal. Fuzzy truth propositions are employed to represent the restrictions attached to a service, and a utility function is used to capture the negotiators preferences over service attributes. The overall acceptability of the proposal can be determined by the restriction obedience and the utility of values for service attributes, collectively. Based on the acceptability of a proposal, a negotiator can decide whether to accept it as a contract, then enact with the party that offers the proposal.