Chi-Hung Chi

Tsinghua University, Peping, Beijing, China

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Publications (139)30.65 Total impact

  • Yunwei Zhao · Chi-Hung Chi · Chen Ding · Raymond Wong · Wei Zhao · Can Wang

    No preview · Conference Paper · Jun 2015
  • Victor Chu · Raymond Wong · Simon Fong · Chi-Hung Chi

    No preview · Article · Jan 2015 · IEEE Transactions on Services Computing
  • Zhiwei Yu · Raymond Wong · Chi-Hung Chi

    No preview · Article · Jan 2015 · IEEE Transactions on Services Computing
  • Masudul Islam · Chen Ding · Chi-Hung Chi

    No preview · Conference Paper · Dec 2014
  • Chi-Hung Chi · Chen Ding · Qing Liu

    No preview · Article · Nov 2014 · Journal of Internet Technology
  • Wei Zhou · Chi-Hung Chi · Can Wang · Raymond Wong · Chen Ding
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    ABSTRACT: Utilizing online spatial data sources to create added values has been quite common in modern Web applications. Through client-side mashup techniques, one can efficiently integrate some popular spatial data services (e.g., Google Maps) through their well-defined interfaces as well as useful tools for mashup. However, many other spatial data providers lack of resources or motivations to provide such rich data services like Google Maps. Instead, they may provide only limited service functionalities, such as static files download only. Furthermore, their data formats and interfaces are vastly heterogeneous. This introduces many more difficulties in data integration, especially for spatial vector data, to which the data accesses often require queries with spatial predicates. Moreover, they may not guarantee system performance in responding client requests. Therefore, all these create a gap between het-erogeneous spatial data sources and mashup applications. To address the problem, we envision a server-side spatial data mashup platform that can provide a unified interface with rich data access functionality on top of these heterogeneous spatial data sources. This paper presents the architecture and a proto-type of such a data mashup platform for spatial vector data specifically. In addition to the typical on-the-fly approach of mashup, the platform can also preload data from data sources with limited system capacities to provide more controllable performance. We demonstrate the effectiveness of this platform through an example web application accessing the integrated data from the platform. This paper further evaluates the system performance and shows the performance tradeoffs of deploying this server-side platform.
    No preview · Conference Paper · Jun 2014
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    ABSTRACT: Recently, researchers have taken tremendous strides in attempting to synthesize conventional social judgments and automated filtering within recommender systems. In this study, we aim to enhance recommendation efficiency via integrating social networking information with traditional recommendation algorithms. To achieve this objective, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships, such as friendship and membership, in measuring the closeness of two users. Subsequently, we define a new item prediction method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on Last.fm show some positive results that attest the efficiency of our proposed approach.
    No preview · Conference Paper · Aug 2013
  • B.S. Vidyalakshmi · Raymond K. Wong · Chi-Hung Chi
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    ABSTRACT: Varieties and volume of content generated from mobile devices contribute to the complications on research and analysis on big data. Increases in content generation through mobile devices, increasing penetration, and decentralized nature are the leading reasons of Peer-to-Peer (P2P) content sharing among mobile devices. With technologies like Bluetooth and NFC paving way for easier and less expensive ad hoc data transfer, content sharing among smartphones is realistic and achievable. Traditional access control among peers in a P2P network assumes that all data is shared among all peers. However, this may not always be the case as data on the smartphones can be personal or confidential. There is a need to address sharing specific data with specific peer, based on peer's trustworthiness with the host peer. We propose a model which controls access to files at a category level rather than at file or user level. We argue that the model preserves peers' autonomy while preserving P2P decentralized structure.
    No preview · Conference Paper · Jun 2013
  • Shuo Chen · Chi-Hung Chi · Chen Ding · Raymond K. Wong
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    ABSTRACT: Nowadays many software services are hosted in the Cloud. When there are more requests on these services, there are also more queries sent to the underlying database. In order to keep up with the increasing workload, it is necessary to have multiple servers hosting the data. Some cloud providers offer the full data replication solution. However, this solution only works when the load mainly consists of the read requests, and when the number of write requests increases, it does not scale well. Although data decomposition has been widely used in data-intensive web sites, not much study has been done on how to decompose the underlying data of software services for the purpose of data replication. In this paper, we propose a data-decomposition-based partial replication model for software services. We devise an automatic algorithm for data decomposition under the constraint of the capacity limit of the host machines. We evaluate our approach from two aspects: scalability and performance, using two benchmarks: RUBiS and TPC-W. In the experiment, we test the algorithm using different workload inputs, and also compare our approach with the full data replication approach.
    No preview · Conference Paper · Jun 2013
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    ABSTRACT: Web services have become a primary mechanism for consuming resources available on the Internet. As more and more services are published on the Web, automated service discovery is critical to consumers to identify relevant and reliable services efficiently. In this paper, we enhance the Web Service Crawler Engine (WSCE) framework by introducing comparison measures to allow for more accurate identification, discovery and ranking of relevant Web services. To discover services effectively, we need to be able to measure and compare the similarity among services. Most ontology-based and IR-based discovery techniques assume that service input/output are simple data types when calculating service similarity. However, real-world services published on the Web usually have complex data types input/output parameters. Furthermore, a good match of parameters does not guarantee good usability and good reliability. The relevant services must be further evaluated by users' past experiences, based on both objective and subjective measures, to make optimal solution selection possible. This paper proposes a service matchmaking algorithm that considers the complex data types of service input/output parameters, as well as experience-based objective and subjective measures for ranking. Experiments show that our approach performs better than previous works that only consider simple data types.
    No preview · Conference Paper · Jun 2013
  • V.W. Chu · R.K. Wong · Chi-Hung Chi
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    ABSTRACT: Due to the popularity of smartphones, finding and recommending suitable services on mobile devices are increasingly important. Recent research has attempted to use role-based approaches to recommend mobile services to other members among the same group in a context dependent manner. However, the traditional role mining approaches originated from the domain of security control tend to be rigid and may not be able to capture human behaviors adequately. In particular, during the course of role mining process, these approaches easily result in over-fitting, i.e., too many roles with slightly different service consumption patterns are found. As a result, they fail to reveal the true common preferences within the user community. This paper proposes an online role mining algorithm with a residual term that automatically group users according to their interests and habits without losing sight of their individual preferences. Moreover, to resolve the over-fitting problem, we relax the role mining mechanism by introducing quasi-roles based on the concept of quasi-bicliques. Most importantly, the new concept allows us to propose a monitoring framework to detect and correct over-fitting in online role mining such that recommendations can be made based on the latest and genuine common preferences. To the best of our knowledge, this is a new area in service recommendation that is yet to be fully explored.
    No preview · Conference Paper · Jan 2013
  • Xiao-lin Zhang · Chi-Hung Chi · Chen Ding · R.K. Wong
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    ABSTRACT: Non-Functional (NF) requirement is very important for the success of a software service. Considering that there could be multiple services implementing a same function, it is crucial for software providers to understand the real NF demands from consumers so that they can meet these demands and attract users. It is also crucial for consumers to know what is being offered so that they can pose realistic NF requests. We address both issues here by proposing a NF requirement analysis and recommendation system which works for both providers and consumers. NF requirements from various sources are first collected, and then we apply the factor analysis technique to identify those independent latent factors which contribute to those observable NF values. Finally we use cluster analysis to summarize the popular NF demands. Our experiment result shows the effectiveness of this approach.
    No preview · Conference Paper · Jan 2013
  • Zhiwei Yu · R.K. Wong · Chi-Hung Chi
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    ABSTRACT: Cloud computing platforms facilitate efficiently processing complicated computing problems of which the time cost used to be unacceptable. Recent research has attempted to use role-based approaches for context-aware service recommendation, yet role mining problem has been proven to be difficult to compute. Currently proposed role-mining algorithms are inefficient and may not scale to cope with the huge amount of data in the real-world. This paper proposes a novel algorithm with much better runtime complexity, and in MapReduce style to take advantage of popular distributed computing platforms. Experiments running on a medium-sized high performance computing cluster demonstrate that our proposed algorithm works well with both running time complexity and scalability.
    No preview · Conference Paper · Jan 2013
  • Victor W. Chu · Raymond K. Wong · Chi-Hung Chi
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    ABSTRACT: Recent research has attempted to use role-based approaches to recommend mobile services to other members among the same group in a context dependent manner. However, the traditional role mining approaches originated from the domain of security control tend to be rigid and may not be able to capture human behaviors adequately. In particular, during the course of role mining process, these approaches easily result in over-fitting, i.e., too many roles with slightly different service consumption patterns are found. As a result, they fail to reveal the true common preferences within the user community. This paper proposes an online role mining algorithm with a residual term and an error term, that automatically group users according to their interests and habits without losing sight of their individual preferences and random errors. Moreover, to resolve the over-fitting problem, the authors relax the role definition in role mining mechanism by introducing quasi-roles based on the concept of quasi-bicliques. Most importantly, the new concept allows us to propose a monitoring framework to detect and correct over-fitting in online role mining such that recommendations can be made based on the latest and genuine common preferences. To the best of the authors' knowledge, this is a new area in service recommendation that is yet to be fully explored.
    No preview · Article · Oct 2012 · International Journal of Web Services Research
  • Anita Mohebi · Chen Ding · Chi-Hung Chi
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    ABSTRACT: Many QoS-based service selection algorithms are complex and time-consuming, and sometimes require a lot of manual efforts from users. As a result, users' real-time searching experiences may not be good when there are many candidate services, because it could be very slow to calculate the ranking scores of services. In this paper, we want to improve the efficiency of the selection process by using a simple vector space model. We also consider the actual QoS requirements from users in the ranking process, which is missing in many current systems. Our experiment results show a big improvement on the system efficiency without losing much on the accuracy when compared with a well-known algorithm -- skyline computation.
    No preview · Conference Paper · Sep 2012
  • Source
    Zhou Wei · Guillaume Pierre · Chi-Hung Chi
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    ABSTRACT: Cloud data stores provide scalability and high availability properties for Web applications, but do not support complex queries such as joins. Web application developers must therefore design their programs according to the peculiarities of No SQL data stores rather than established software engineering practice. This results in complex and error-prone code, especially with respect to subtle issues such as data consistency under concurrent read/write queries. We present join query support in Cloud TPS, a middleware layer which stands between a Web application and its data store. The system enforces strong data consistency and scales linearly under a demanding workload composed of join queries and read-write transactions. In large-scale deployments, Cloud TPS outperforms replicated Postgre SQL up to three times.
    Full-text · Conference Paper · May 2012
  • Rozita Mirmotalebi · Chen Ding · Chi-Hung Chi
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    ABSTRACT: Modeling users’ online behavior has great benefit for many e-Commerce web sites and search engines. In the context of software service selection, if we could understand users’ personal preferences, we could rank the services in a more satisfactory way. Many users have some general preferences on the desired values of non-functional properties (e.g. provider history, service popularity, etc.) of services, even if they may not explicitly define them. In this paper, we propose to build user profiles on these non-functional preferences, and then use them to personalize the ranking results for individual users. Our experiment showed that personalized ranking could promote the services matching with the user preferred non-functional values to higher positions, making it easier for users to identify their desired services. We also tested how different factors impact the degree of improvement on the ranking accuracy.
    No preview · Chapter · Jan 2012
  • Donghong Huang · Chi-Hung Chi
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    ABSTRACT: Agent-based modeling is a powerful method for studying user's behaviors in online social networks. Agent uses its value system to estimate the value of its behavior. A good value system model for agent can make the agent more intelligence and hence facilitate the development of intelligence system. However, the issue of modeling agent's value system is not properly studied yet. In this paper, we propose a mathematical value system model for estimating the value of agent's behavior in online social network. We use three evaluation dimensions as the universal equivalents for behaviors, and propose a weight function mechanism to estimate each dimension's weight. We show the potential usage of our model by applying it to the case of online word-of-mouth advertising.
    No preview · Conference Paper · Dec 2011
  • Raed Karim · Chen Ding · Chi-Hung Chi
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    ABSTRACT: In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple non-functional criteria they require on the target service. It is a natural fit to apply the Multi-Criteria Decision Making (MCDM) theory to this selection problem. However, the high demand of MCDM approaches on user expertise and user involvement could become an obstacle of using them for service selection. In this paper, we address this issue by taking a user-centric standpoint to design the non-functional criteria based service selection system. On one hand, we try to reduce the workload and the skill level requirement on users. On the other hand, we still give them the flexibility to define the necessary information, which include their preferences on multiple criteria, as well as the decision strategies they would follow to select the desired services from a list of alternatives. The former is crucial for optimal decision making. The latter is often ignored by most of the service selection systems and a common default selection strategy is to find a service which has the best overall score calculated by a certain formula. In reality, users may not necessarily follow this strategy and there are many other possible strategies they may follow. We should take this into consideration when designing selection systems. We use a case study to show that our system could produce a more accurate and customized result for individual users.
    No preview · Article · Dec 2011
  • Yun Wei Zhao · Chi-Hung Chi · Chen Ding
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    ABSTRACT: Clustering is an important technique for intelligence computation such as trust, recommendation, reputation, and requirement elicitation. With the user centric nature of service and the user's lack of prior knowledge on the distribution of the raw data, one challenge is on how to associate user quality requirements on the clustering results with the algorithmic output properties (e.g. number of clusters to be targeted). In this paper, we focus on the hierarchical clustering process and propose two quality-driven hierarchical clustering algorithms, HBH (homogeneity-based hierarchical) and HDH (homogeneity-driven hierarchical) clustering algorithms, with minimum acceptable homogeneity and relative population for each cluster output as their input criteria. Furthermore, we also give a HDH-approximation algorithm in order to address the time performance issue. Experimental study on data sets with different density distribution and dispersion levels shows that the HDH gives the best quality result and HDH-approximation can significantly improve the execution time.
    No preview · Conference Paper · Oct 2011