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On the Profitability of Bundling Sale Strategy for Online Service Markets With Network Effects

In recent years, we have witnessed a growing trend for online service companies to offer... (more)

A Dynamic Data-throttling Approach to Minimize Workflow Imbalance

Scientific workflows enable scientists to undertake analysis on large datasets and perform complex scientific simulations. These workflows are often... (more)

Social Network De-anonymization: More Adversarial Knowledge, More Users Re-identified?

Previous works on social network de-anonymization focus on designing accurate and efficient de-anonymization methods. We attempt to investigate the intrinsic relationship between the attacker’s knowledge and the expected de-anonymization gain. A common intuition is that more knowledge results in more successful de-anonymization. However,... (more)

Integrating Multi-level Tag Recommendation with External Knowledge Bases for Automatic Question Answering

We focus on using natural language unstructured textual Knowledge Bases (KBs) to answer questions... (more)

Constructing Novel Block Layouts for Webpage Analysis

Webpage segmentation is the basic building block for a wide range of webpage analysis methods. The rapid development of Web technologies results in... (more)

Mitigating Tail Response Time of n-Tier Applications: The Impact of Asynchronous Invocations

Consistent low response time is essential for e-commerce due to intense competitive pressure. However, practitioners of web applications have often... (more)

Source-Aware Crisis-Relevant Tweet Identification and Key Information Summarization

Twitter is an important source of information that people frequently contribute to and rely on for emerging topics, public opinions, and event... (more)

CloseUp—A Community-Driven Live Online Search Engine

Search engines are still the most common way of finding information on the Web. However, they are largely unable to provide satisfactory answers to... (more)

Policy Adaptation in Hierarchical Attribute-based Access Control Systems

In Attribute-Based Access Control (ABAC), access to resources is given based on the attributes of subjects, objects, and environment. There is an... (more)

Threat Management in Data-centric IoT-Based Collaborative Systems

In this article, we propose a threat management system (TMS) for Data-centric Internet-of-Things-based Collaborative Systems (DIoTCSs). In particular,... (more)

Multi-objective Optimisation of Online Distributed Software Update for DevOps in Clouds

This article studies synchronous online distributed software update, also known as rolling upgrade in DevOps, which in clouds upgrades software... (more)

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About TOIT

ACM Transactions on Internet Technology (TOIT) brings together many computing disciplines that contribute to Internet systems and technologies, including computer software engineering, computer programming languages, computer middleware and systems, computer networking and communications, database management, distributed systems, knowledge discovery and data mining, machine learning and AI as a service, security, privacy, performance and scalability, etc. TOIT welcomes the innovative research results from both the individual disciplines and the interactions among them.

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Forthcoming Articles
CAN-TM: Chain Augmented Naïve Bayes-based Trust Model for reliable cloud service selection

The increasing proliferation of Cloud Services (CSs) has made the reliable CS selection problem a major challenge. To tackle this problem, this article introduces an effective trust model called Chain Augmented Naïve Bayes-based Trust Model (CAN-TM). This model leverages the correlation that may exist among QoS attributes to solve many issues in reliable CS selection challenge such as predicting missing assessments, and improving accuracy of trust computing. This is achieved by combining both the n-gram Markov model and the Naïve Bayes model. Experiments are conducted to validate that our proposed CAN-TM outperforms state-of-the-art approaches.

An Incentive Mechanism for Crowdsourcing Systems with Network Effects

In a crowdsourcing system, it is important for the crowdsourcer to engineer extrinsic rewards to incentivize the participants. In this paper, we incorporate network effects as a contributing factor to intrinsic rewards, and study its influence on the design of extrinsic rewards. We show that the number of participating users and their contributions to the crowdsourcing system evolve to a steady equilibrium. We design progressively more sophisticated extrinsic reward mechanisms, and propose new and optimal strategies for a crowdsourcer to obtain a higher utility. Through simulations, we demonstrate that with our new strategies, a crowdsourcer can attract more participants.

Betweenness Centrality Based Software Defined Routing: Observation from Practical Internet Datasets

In this paper, we make an in-depth observation of practical Internet datasets, and investigate the relationship between betweenness centrality and network throughput. Furthermore, we propose a new routing observation factor, di?erential-ratio-of-betweenness-centrality (DRBC), to denote the varying amplitude of betweenness centrality to node degree. We find an interesting phenomenon that DRBC is proportional to the routing efciency when the maximum betweenness centrality varies in a small range. Based on this, a DRBC-based routing scheme is proposed to improve network throughput. The experimental results verify that DRBC-based routing can improve the routing efciency and accelerate the routing optimization.

Introduction to the Special Section on Advances in Internet-Based Collaborative Technologies

Universal Social Network Bus: Towards the Federation of Heterogeneous Online Social Network Services

Online social network services (OSNSs) are changing the fabric of our society, impacting almost every aspect of it. Over the last decades, multiple competing OSNSs have emerged. As a result, users are trapped in the walled gardens of their OSNS, encountering restrictions about the people they can interact with. Our work aims at enabling users to meet and interact beyond the boundary of their OSNSs. We introduce USNB -Universal Social Network Bus which revisits the "service bus" paradigm to address the requirements of social interoperability.

Trust Prediction via Matrix Factorization

We propose PTP-MF (Pairwise Trust Prediction through Matrix Factorization), an algorithm to predict the intensity of trust/distrust relations in Online Social Networks. The PTP-MF algorithm maps each user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust others) and the trustee profile (modelling how others perceive i as trustworthy) and computes the trust j places in j as the dot product of trustor profile of i and the trustee profile of j. Experiments indicate that the PTP-MF algorithm is more accurate than the state-of-the-art approaches (up to 9.65%) and scales well on real life graphs

Pay as Your Service Needs: An Application-Driven Pricing Approach for the Internet Economics

Various differentiated pricing schemes have been proposed for the Internet market. Aiming at replacing the traditional single-class pricing for better welfare, yet, researchers have shown that existing schemes can bring only marginal profit gain for the ISPs. We point out that a proper form of differentiated pricing for the Internet should not only consider congestion, but also provide application specific treatment to data delivery. Formally, we propose an ?application-driven pricing? approach. Opposite from previous studies, we show that the revenue gain of multi-class pricing under our scheme can be significant. We also identify key factors that impact the revenue gain.

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