Exploring the measurement features of usage data for academic literature

The emergence of new data has always been a major force driving the advance of informetrics. The magnitude of data usage for academic literature, given by the Web of Science(Wo S), can be expected to provide a supplementary perspective for citation analysis in three theoretical aspects, which are influence dimension, discipline difference and behavioral motive. Based on 166,767 articles in physics, computer science, economics, and Library and Information Science included by SCI and SSCI in 2013 as research samples, this paper conducts an exploratory investigation into the measurement features of the usage data. According to the results, compared with citation data, usage is more discriminative and sensitive. In terms of distribution, usage presents an approximate and positively skewed distribution at high-frequency. An approximate power-law distribution is observed in the cumulative integral. The evaluation result of usage is independent to a certain degree and poses no essential opposition to the result of citation. Although the usage of the Wo S platform still has the limitations of generality, falsifiability and isolation, usage may be accepted as one of the possible options that supplement citation data and provide a richer field of vision for influence evaluation. There are 4 figures, 6 tables and 23 references in this paper.

TrustControl:Trusted Private Data Usage Control Based on Security Enhanced TrustZone

The past decade has seen the rapid development of data in many areas.Data has enormous commercial potential as a new strategic resource that may efficiently boost technical growth and service innovation.However,individuals are becoming increasingly concerned about data misuse and leaks.To address these issues,in this paper,we propose TrustControl,a trusted data usage control system to control,process,and protect data usage without revealing privacy.A trusted execution environment(TEE)is exploited to process confidential user data.First of all,we design a secure and reliable remote attestation mechanism for ARM TrustZone,which can verify the security of the TEE platform and function code,thus guaranteeing data processing security.Secondly,to address the security problem that the raw data may be misused,we design a remote dynamic code injection method to regulate that data can only be processed for the expected purpose.Our solution focuses on protecting the sensitive data of the data owner and the function code of the data user to prevent data misuse and leakage.Furthermore,we implement the prototype system of TrustControl on TrustZone-enabled hardware.Real-world experiment results demonstrate that the proposed Trust-Control is secure and the performance overhead of introducing our prototype system is very low.

Parallelized User Clicks Recognition from Massive HTTP Data Based on Dependency Graph Model

With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.

Exploring the user platform preference and user interest preference of Chinese scholarly articles:A comparison based on usage metrics

In order to comparatively investigate usage patterns of journal official sites and pay-for-access platform and then to support decision-making,this study selected academic papers,published during 2014-2015,of 61 Chinese open access journals indexed by CSSCI and CSCD in eight disciplines,"Library,Information and Archival Science","Management Science","Economics","Pedagogy","Computer Science","Earth Science","Math"and"Biology".Chinese usage patterns in user platform preferences and user interest preferences were explored by analyzing usage data from journal official sites and pay-for-access platforms.Operatively,comparisons of user platform preferences were implemented by descriptive statistics and correlation analysis and comparisons of user interest preferences were explored by Jaccard similarity coefficient and co-word analysis.It proved that there were differences in user platform preferences and user interest preferences between journal official sites and pay-for-access platforms.However,factors that affect usage patterns were very complicated.How to probe into the mechanism of contributory factors and then verify them was still an important problem to be solved in further study.

Roundtable： Research Opportunities and Challenges for Large-Scale Software Systems

For this special section on software systems, six research leaders in software systems, as guest editors tor this special section, discuss important issues that will shape this field＇s future research directions. The essays included in this roundtable article cover research opportunities and challenges for large-scale software systems such as querying organization- wide software behaviors （Xusheng Xiao）, logging and log analysis （Jian-Ouang Lou）, engineering reliable cloud distributed systems （Shan Lu）, usage data （David C. Shepherd）, clone detection and management （Xin Peng）, and code search and beyond （Qian-Xiang Wang）. - Tao Xie, Leading Editor of Software Systems.