NDLI: Mining Context-Aware User Requirements from Crowd Contributed Mobile Data

Please wait, while we are loading the content...

DelayDroid: Reducing Tail-Time Energy by Refactoring Android Apps

Enhancing Defect Prediction with Static Defect Analysis

Exploring the Use of @-mention to Assist Software Development in GitHub

A Creative Lifecycle Model for Internetware Based Software Development

Efficient Testing of Web Services with Mobile Crowdsourcing

A Feature-Driven Approach to Automated Class Diagram Construction

CBBR: Enabling Distributed Shared Memory-based Coordination Among Mobile Robots

Towards A Novel Approach for Defect Localization Based on Part-of-Speech and Invocation

GEMiner: Mining Social and Programming Behaviors to Identify Experts in Github

Mining Context-Aware User Requirements from Crowd Contributed Mobile Data

Facilitating Reusable and Scalable Automated Testing and Analysis for Android Apps

Improving BDD-based Attractor Detection for Synchronous Boolean Networks

Towards Web Application Mobilization via Efficient Web Control Extraction

Detecting Buggy Files based on Bug Reports: A Random Walk Based Approach

Towards a scalable and efficient open cloud marketplace

A hybrid static analysis refinement approach within internetware environment

nCov: A Tool for Measuring Length-n Subpath Coverage

Analyzing Inductively Defined Properties for Recursive Data Structures

A Study on Power Side Channels on Mobile Devices

Evaluating Bug Severity Using Crowd-based Knowledge: An Exploratory Study

Software Ranking and Analysis based on Mining Market Requirements and Characteristics

A supervised approach for tag hierarchy construction in open source communities

A Multi-objective Ant Colony Algorithm for Deployment Optimization of Internetware Application

Towards Realisation of Evolvable Runtime Variability in Internet-Based Service Systems via Dynamical Software Update

Qos-aware Automatic Web Service Composition Considering QoS Correlations

Cross-Project Software Defect Prediction Using Feature-Based Transfer Learning

Combining Crowd Contributions with Machine Learning to Detect Malicious Mobile Apps

An Entropy-based Approach to the Crowd Entity Resolution

Invariant Synthesis for Programs Manipulating Arrays with Unbounded Data

A Crowdsourcing framework for Detecting Cross-Browser Issues in Web Application

Detecting Data Races in Interrupt-Driven Programs based on Static Analysis and Dynamic Simulation

Mining Context-Aware User Requirements from Crowd Contributed Mobile Data

Content Provider	ACM Digital Library
Author	Qian, Wenyi Zhao, Wenyun Liang, Wenjie Wu, Yijian Peng, Xin
Abstract	Internetware is required to respond quickly to emergent user requirements or requirements changes by providing application upgrade or making context-aware recommendations. As user requirements in Internet computing environment are often changing fast and new requirements emerge more and more in a creative way, traditional requirements engineering approaches based on requirements elicitation and analysis cannot ensure the quick response of Internetware. In this paper, we propose an approach for mining context-aware user requirements from crowd contributed mobile data. The approach captures behavior records contributed by a crowd of mobile users and automatically mines context-aware user behavior patterns (i.e., when, where and under what conditions users require a specific service) from them using Apriori-M algorithm. Based on the mined user behaviors, emergent requirements or requirements changes can be inferred from the mined user behavior patterns and solutions that satisfy the requirements can be recommended to users. To evaluate the proposed approach, we conduct an experimental study and show the effectiveness of the requirements mining approach.
Starting Page	132
Ending Page	140
Page Count	9
File Format	PDF
ISBN	9781450336413
DOI	10.1145/2875913.2875933
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Requirement mining Service recommendation Data mining Mobile computing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Context-Aware Service Recommendation on High Performance Cluster

Location Based Context Aware user Interface Recommendation System

Context-aware role mining for mobile service recommendation

Just-In-Time Recommendation Approach Within A Mobile Context

Design and implementation of user context aware recommendation engine for mobile using bayesian network, fuzzy logic and rule base.

Mining Mobile User Preferences for Personalized Context-Aware Recommendation

Mining Personal Context-Aware Preferences for Mobile Users

Keynote: Context-aware computing in the era of crowd sensing from personal and space context to social and community context

Crowd Out the Competition

Mining Context-Aware User Requirements from Crowd Contributed Mobile Data

Similar Documents

Context-Aware Service Recommendation on High Performance Cluster

Location Based Context Aware user Interface Recommendation System

Context-aware role mining for mobile service recommendation

Just-In-Time Recommendation Approach Within A Mobile Context

Design and implementation of user context aware recommendation engine for mobile using bayesian network, fuzzy logic and rule base.

Mining Mobile User Preferences for Personalized Context-Aware Recommendation

Mining Personal Context-Aware Preferences for Mobile Users

Keynote: Context-aware computing in the era of crowd sensing from personal and space context to social and community context

Crowd Out the Competition

Mining Context-Aware User Requirements from Crowd Contributed Mobile Data