6,515 research outputs found
Datamining for Web-Enabled Electronic Business Applications
Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business
Data-Driven Application Maintenance: Views from the Trenches
In this paper we present our experience during design, development, and pilot
deployments of a data-driven machine learning based application maintenance
solution. We implemented a proof of concept to address a spectrum of
interrelated problems encountered in application maintenance projects including
duplicate incident ticket identification, assignee recommendation, theme
mining, and mapping of incidents to business processes. In the context of IT
services, these problems are frequently encountered, yet there is a gap in
bringing automation and optimization. Despite long-standing research around
mining and analysis of software repositories, such research outputs are not
adopted well in practice due to the constraints these solutions impose on the
users. We discuss need for designing pragmatic solutions with low barriers to
adoption and addressing right level of complexity of problems with respect to
underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th
International Workshop on Software Engineering Research and Industrial
Practice (SER&IP), IEEE Press, pp. 48-54, 201
SOTXTSTREAM: Density-based self-organizing clustering of text streams
A streaming data clustering algorithm is presented building upon the density-based selforganizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clustering algorithms use a two-phase clustering approach. In the first phase, a micro-clustering solution is maintained online, while in the second phase, the micro-clustering solution is clustered offline to produce a macro solution. By performing self-organization techniques on micro-clusters in the online phase, SOSTREAM is able to maintain a macro clustering solution in a single phase. Leveraging concepts from SOSTREAM, a new density-based self-organizing text stream clustering algorithm, SOTXTSTREAM, is presented that addresses several shortcomings of SOSTREAM. Gains in clustering performance of this new algorithm are demonstrated on several real-world text stream datasets
User Review-Based Change File Localization for Mobile Applications
In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table
Insights from Machine-Learned Diet Success Prediction
To support people trying to lose weight and stay healthy, more and more
fitness apps have sprung up including the ability to track both calories intake
and expenditure. Users of such apps are part of a wider ``quantified self''
movement and many opt-in to publicly share their logged data. In this paper, we
use public food diaries of more than 4,000 long-term active MyFitnessPal users
to study the characteristics of a (un-)successful diet. Concretely, we train a
machine learning model to predict repeatedly being over or under self-set daily
calories goals and then look at which features contribute to the model's
prediction. Our findings include both expected results, such as the token
``mcdonalds'' or the category ``dessert'' being indicative for being over the
calories goal, but also less obvious ones such as the difference between pork
and poultry concerning dieting success, or the use of the ``quick added
calories'' functionality being indicative of over-shooting calorie-wise. This
study also hints at the feasibility of using such data for more in-depth data
mining, e.g., looking at the interaction between consumed foods such as mixing
protein- and carbohydrate-rich foods. To the best of our knowledge, this is the
first systematic study of public food diaries.Comment: Preprint of an article appearing at the Pacific Symposium on
Biocomputing (PSB) 2016 in the Social Media Mining for Public Health
Monitoring and Surveillance trac
Loghub: A Large Collection of System Log Datasets towards Automated Log Analytics
Logs have been widely adopted in software system development and maintenance
because of the rich system runtime information they contain. In recent years,
the increase of software size and complexity leads to the rapid growth of the
volume of logs. To handle these large volumes of logs efficiently and
effectively, a line of research focuses on intelligent log analytics powered by
AI (artificial intelligence) techniques. However, only a small fraction of
these techniques have reached successful deployment in industry because of the
lack of public log datasets and necessary benchmarking upon them. To fill this
significant gap between academia and industry and also facilitate more research
on AI-powered log analytics, we have collected and organized loghub, a large
collection of log datasets. In particular, loghub provides 17 real-world log
datasets collected from a wide range of systems, including distributed systems,
supercomputers, operating systems, mobile systems, server applications, and
standalone software. In this paper, we summarize the statistics of these
datasets, introduce some practical log usage scenarios, and present a case
study on anomaly detection to demonstrate how loghub facilitates the research
and practice in this field. Up to the time of this paper writing, loghub
datasets have been downloaded over 15,000 times by more than 380 organizations
from both industry and academia.Comment: Dateset available at https://zenodo.org/record/322717
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