Elysium Technologies Private...




The last decade has witnessed a tremendous growth of Web services as a major technology for sharing data,

computing resources, and programs on the Web. With the increasing adoption and presence of Web services,

design of novel approaches for effective Web service recommendation to satisfy users’ potential requirements

has become of paramount importance. Existing Web service recommendation approaches mainly focus on

predicting missing QoS values of Web service candidates which are interesting to a user using collaborative

filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that

recommended Web services are independent to each other, which sometimes may not be true. As a result,

many similar or redundant Web services may exist in a recommendation list. In this paper, we propose a novel

Web service recommendation approach incorporating a user’s potential QoS preferences and diversity feature

of user interests on Web services. User’s interests and QoS preferences on Web services are first mined by

exploring the Web service usage history. Then we compute scores of Web service candidates by measuring

their relevance with historical and potential user interests, and their QoS utility. We also construct a Web

service graph based on the functional similarity between Web services. Finally, we present an innovative

diversity-aware Web service ranking algorithm to rank the Web service candidates based on their scores, and

diversity degrees derived from the Web service graph. Extensive experiments are conducted based on a real

world Web service dataset, indicating that our proposed Web service recommendation approach significantly

improves the quality of the recommendation results compared with existing methods.

ETPL

WS - 001

Diversifying Web Service Recommendation Results via Exploring Service Usage

History

Selecting an optimal web service among a list of functionally equivalent web services still remains a

challenging issue. For Internet services, the presence of low-performance servers, high latency or overall poor

service quality can translate into lost sales, user frustration, and customers lost. In this paper, we propose a

novel method for QoS metrification based on Hidden Markov Models (HMM), which further suggests an

optimal path for the execution of user requests. The technique we show can be used to measure and predict the

behavior of Web Services in terms of response time, and can thus be used to rank services quantitatively rather

than just qualitatively. We demonstrate the feasibility and usefulness of our methodology by drawing

experiments on real world data. The results have shown how our proposed method can help the user to

automatically select the most reliable Web Service taking into account several metrics, among them, system

predictability and response time variability. Later ROC curve shows a 12 percent improvement in prediction

accuracy using HMM

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WS - 002

Response Time Based Optimal Web Service Selection








Web service recommendation is one of the most important fields of research in the area of service computing.

The two core problems of Web service recommendation are the prediction of unknown QoS property values

and the evaluation of overall QoS according to user preferences. Aiming to address these two problems and

their current challenges, we propose two efficient approaches to solve these problems. First, unknown QoS

property values were predicted by modeling the high-dimensional QoS data as tensors, by utilizing an

important tensor operation, i.e., tensor composition, to predict these QoS values. Our method, which considers

all QoS dimensions integrally and uniformly, allows us to predict multi-dimensional QoS values accurately

and easily. Second, the overall QoS was evaluated by proposing an efficient user preference learning method,

which learns user preferences based on users ratings history data, allowing us to obtain user preferences

quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic

value for the overall QoS. The experimental results showed our proposed methods to be more efficient than

existing methods.

ETPL

WS - 003

QoS evaluation for web service recommendation

Many web computing systems are running real time database services where their information change

continuously and expand incrementally. In this context, web data services have a major role and draw

significant improvements in monitoring and controlling the information truthfulness and data propagation.

Currently, web telemedicine database services are of central importance to distributed systems. However, the

increasing complexity and the rapid growth of the real world healthcare challenging applications make it hard

to induce the database administrative staff. In this paper, we build an integrated web data services that satisfy

fast response time for large scale Tele-health database management systems. Our focus will be on database

management with application scenarios in dynamic telemedicine systems to increase care admissions and

decrease care difficulties such as distance, travel, and time limitations. We propose three-fold approach based

on data fragmentation, database websites clustering and intelligent data distribution. This approach reduces the

amount of data migrated between websites during applications' execution; achieves cost-effective

communications during applications' processing and improves applications' response time and throughput. The

proposed approach is validated internally by measuring the impact of using our computing services' techniques

on various performance features like communications cost, response time, and throughput. The external

validation is achieved by comparing the performance of our approach to that of other techniques in the

literature. The results show that our integrated approach significantly improves the performance of web

database systems and outperforms its counterparts.

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BD - 004

Designing High Performance Web-Based Computing Services to Promote

Telemedicine Database Management System








A customizable Web service is a service that enables service consumers to dynamically determine variants of

the service they receive. Provisioning customizable services helps to efficiently address functional variability

in customer requirements. However, this is challenging due to: i) the complexity in deriving the right subset of

service capabilities for a service variant and ii) the existence of a large number of variants and their

dependencies. We propose a feature-based framework to tackle this challenge. In our framework, a feature

model is used to capture functional variability in customer requirements at a high-level of abstraction and to

provide customers with a much simpler way to customize an atomic service. A service engineering process is

designed to facilitate the systematic identification and implementation of variability during service

development, and to maintain the mapping between variabilities at the feature modeling level and the service

implementation level. We define a generative middleware that supports service deployment and exploits the

mapping to enable runtime service customization. A large scale case study based on the Amazon Web

Services is used for evaluation. In addition to addressing the challenge in provisioning customizable services,

our experiments show that the generative middleware helps to reduce runtime resource consumption.

With the emergence of the open data movement, hundreds of thousands of datasets from various concerns are

now freely available on the Internet. The access to a good number of these datasets is carried out through Web

services which provide a standard way to interact with data. In this context, user's queries often require the

composition of multiple data Web services to be answered. Defining the semantics of data services is the first

step towards automating their composition. An interesting approach to define the semantics of data services is

by describing them as semantic views over a domain ontology. However, defining such semantic views cannot

always be done with certainty, especially when the service's outputs are too complex. In this paper, we propose

a probabilistic approach to model the semantics uncertainty of data services. In our approach, a data service

with an uncertain semantics is described by several possible semantic views, each one is associated with a

probability. Services along with their possible semantic views are represented in a Block-Independent-Disjoint

(noted BID) probabilistic service registry, and interpreted based on the Possible Worlds Semantics. Based on

our modeling, we study the problem of interpreting an existing composition involving services with uncertain

semantics. We also study the problem of compositing uncertain data services to answer a user query, and

propose an efficient method to compute the different possible compositions and their probabilities.

ETPL

WS - 005

Composing Data Services with Uncertain Semantics

ETPL

WS - 006

A Feature-Based Framework for Developing and Provisioning Customizable

Web Services








Web application development benefits massively from modular architectures and reuse. This excellent

software engineering practice is also the source of a new form of monoculture in application-level co de,

which creates a potential risk for dependability. Researchers propose using software diversification in multiple

components of Web applications to reconcile the tension between reuse and dependability. This article

identifies key enablers for the effective diversification of software, especially at the application-code level. It's

possible to combine different software diversification strategies, from deploying different vendor solutions to

fine-grained code transformations, to provide different forms of protection.

ETPL

WS - 007

Multitier Diversification in Web-Based Software Applications

A large-scale resource sharing system (e.g., collaborative cloud computing and grid computing) creates a

virtual supercomputer by providing an infrastructure for sharing tremendous amounts of resources (e.g.,

computing, storage, and data) distributed over the Internet. A resource information service, which collects

resource data and provides resource search functionality for locating desired resources, is a crucial component

of the resource sharing system. In addition to resource discovery speed and cost (i.e., efficiency), the ability to

accurately locate all satisfying resources (i.e., fidelity) is also an important metric for evaluating service

quality. Previously, a number of resource information service systems have been proposed based on

Distributed Hash Tables (DHTs) that offer scalable key-based lookup functions. However, these systems

either achieve high fidelity at low efficiency, or high efficiency at low fidelity. Moreover, some systems have

limited flexibility by only providing exact-matching services or by describing a resource using a pre-defined

list of attributes. This paper presents a resource information service that offers high efficiency and fidelity

without restricting resource expressiveness, while also providing a similar-matching service. Extensive

simulation and PlanetLab experimental results show that the proposed service outperforms other services in

terms of efficiency, fidelity, and flexibility; it dramatically reduces overhead and yields significant

enhancements in efficiency and fidelity.

ETPL

WS - 008

Combining Efficiency, Fidelity, and Flexibility in Resource Information Services








This paper investigates the problem of celebrity face naming in unconstrained videos with user-

provided metadata . Instead of relying on accurate face labels for supervised learning, a rich set of

relationships automatically derived from video content and knowledge from image domain and social

cues is leveraged for unsupervised face labeling. The relationships refer to the appearances of faces

under different spatio-temporal contexts and their visual similarities. The knowledge includes Web

images weakly tagged with celebrity names and the celebrity social networks. The relationships and

knowledge are elegantly encoded using conditional random field (CRF) for label inference. Two

versions of face annotation are considered: within-video and between-video face labeling. The former

addresses the problem of incomplete and noisy labels in metadata, where null assignment of names is

allowed-a problem seldom been considered in the literature. The latter further rectifies the errors in

metadata, specifically to correct false labels and annotate faces with missing names in the metadata

of a video, by considering a group of socially connected videos for joint label inference.

Experimental results on a large archive of Web videos show the robustness of the proposed approach

in dealing with the problems of missing and false labels, leading to higher accuracy in face labeling

than several existing approaches but with minor degradation in speed efficiency.

ETPL

WS - 009

Unsupervised Celebrity Face Naming in Web Videos

ETPL

WS - 010






















ETPL

WS - 011

Response Time Based Optimal Web Service Selection

Selecting an optimal web service among a list of functionally equivalent web services still remains a

challenging issue. For Internet services, the presence of low-performance servers, high latency or overall poor

service quality can translate into lost sales, user frustration, and customers lost. In this paper, we propose a

novel method for QoS metrification based on Hidden Markov Models (HMM), which further suggests an

optimal path for the execution of user requests. The technique we show can be used to measure and predict the

behavior of Web Services in terms of response time, and can thus be used to rank services quantitatively rather

than just qualitatively. We demonstrate the feasibility and usefulness of our methodology by drawing

experiments on real world data. The results have shown how our proposed method can help the user to

automatically select the most reliable Web Service taking into account several metrics, among them, system

predictability and response time variability. Later ROC curve shows a 12 percent improvement in prediction

accuracy using HMM.

ETPL

WS - 012

SmartCrawler: A Two-stage Crawler for Efficiently Harvesting Deep-Web

Interfaces

As deep web grows at a very fast pace, there has been increased interest in techniques that help efficiently

locate deep-web interfaces. However, due to the large volume of web resources and the dynamic nature of

deep web, achieving wide coverage and high efficiency is a challenging issue. We propose a two-stage

framework, namely SmartCrawler, for efficient harvesting deep web interfaces. In the first stage,

SmartCrawler performs site-based searching for center pages with the help of search engines, avoiding visiting

a large number of pages. To achieve more accurate results for a focused crawl, SmartCrawler ranks websites

to prioritize highly relevant ones for a given topic. In the second stage, SmartCrawler achieves fast in-site

searching by excavating most relevant links with an adaptive link-ranking. To eliminate bias on visiting some

highly relevant links in hidden web directories, we design a link tree data structure to achieve wider coverage

for a website. Our experimental results on a set of representative domains show the agility and accuracy of our

proposed crawler framework, which efficiently retrieves deep-web interfaces from large-scale sites and

achieves higher harvest rates than other crawlers.








ETPL

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proposed approach is validated internally by measuring the impact of using our computing services' techniques

on various performance features like communications cost, response time, and throughput. The external

validation is achieved by comparing the performance of our approach to that of other techniques in the

literature. The results show that our integrated approach significantly improves the performance of web

database systems and outperforms its counterparts.

ETPL

WS - 014

QoS evaluation for web service recommendation

Web service recommendation is one of the most important fields of research in the area of service computing.

The two core problems of Web service recommendation are the prediction of unknown QoS property values

and the evaluation of overall QoS according to user preferences. Aiming to address these two problems and

their current challenges, we propose two efficient approaches to solve these problems. First, unknown QoS

property values were predicted by modeling the high-dimensional QoS data as tensors, by utilizing an

important tensor operation, i.e., tensor composition, to predict these QoS values. Our method, which considers

all QoS dimensions integrally and uniformly, allows us to predict multi-dimensional QoS values accurately

and easily. Second, the overall QoS was evaluated by proposing an efficient user preference learning method,

which learns user preferences based on users??? ratings history data, allowing us to obtain user preferences

quantifiably and accurately. By solving these two core problems, it became possible to compute a realistic

value for the overall QoS. The experimental results showed our proposed methods to be more efficient than

existing methods.








ETPL

WS - 015

An Integrated Semantic Web Service Discovery and Composition Framework

In this paper we present a theoretical analysis of graph-based service composition in terms of its

dependency with service discovery. Driven by this analysis we define a composition framework by

means of integration with fine-grained I/O service discovery that enables the generation of a graph-

based composition which contains the set of services that are semantically relevant for an input-

output request. The proposed framework also includes an optimal composition search algorithm to

extract the best composition from the graph minimising the length and the number of services, and

different graph optimisations to improve the scalability of the system. A practical implementation

used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of

our proposal and provides insights on how integrated composition systems can be designed in order

to achieve good performance in real scenarios for the Web.

ETPL

WS - 016

Location-Aware and Personalized Collaborative Filtering for Web Service

Recommendation

Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web

service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although

several CF-based Web service QoS prediction methods have been proposed in recent years, the performance

still needs significant improvement. Firstly, existing QoS prediction methods seldom consider personalized

influence of users and services when measuring the similarity between users and between services. Secondly,

Web service QoS factors, such as response time and throughput, usually depends on the locations of Web

services and users. However, existing Web service QoS prediction methods seldom took this observation into

consideration. In this paper, we propose a location-aware personalized CF method for Web service

recommendation. The proposed method leverages both locations of users and Web services when selecting

similar neighbors for the target user or service. The method also includes an enhanced similarity measurement

for users and Web services, by taking into account the personalized influence of them. To evaluate the

performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web

service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and

computational efficiency significantly, compared to previous CF-based methods.








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