Open Access. Powered by Scholars. Published by Universities.®
Databases and Information Systems Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Association Measures (1)
- Association measures (1)
- Automated detection (1)
- Data mining (1)
- Debugging (1)
-
- Factorization machine (1)
- Fault Localization (1)
- Fault location (1)
- Frequent subgraph mining (1)
- Hierarchy (1)
- Importance weight (1)
- Layered architecture (1)
- Local structures (1)
- Mobile advertising (1)
- Network robustness (1)
- Non-trivial (1)
- Normalized Laplacian matrix (1)
- Program Spectra (1)
- R-energy (1)
- Response prediction (1)
- Social network analysis (1)
- Social networks (1)
- Software project (1)
- Statistical analysis (1)
- Variables of interest (1)
Articles 1 - 9 of 9
Full-Text Articles in Databases and Information Systems
Predicting Response In Mobile Advertising With Hierarchical Importance-Aware Factorization Machine, Richard Jayadi Oentaryo, Ee Peng Lim, Jia Wei Low, David Lo, Michael Finegold
Predicting Response In Mobile Advertising With Hierarchical Importance-Aware Factorization Machine, Richard Jayadi Oentaryo, Ee Peng Lim, Jia Wei Low, David Lo, Michael Finegold
David LO
Mobile advertising has recently seen dramatic growth, fueled by the global proliferation of mobile phones and devices. The task of predicting ad response is thus crucial for maximizing business revenue. However, ad response data change dynamically over time, and are subject to cold-start situations in which limited history hinders reliable prediction. There is also a need for a robust regression estimation for high prediction accuracy, and good ranking to distinguish the impacts of different ads. To this end, we develop a Hierarchical Importance-aware Factorization Machine (HIFM), which provides an effective generic latent factor framework that incorporates importance weights and hierarchical …
R-Energy For Evaluating Robustness Of Dynamic Networks, Ming Gao, Ee Peng Lim, David Lo
R-Energy For Evaluating Robustness Of Dynamic Networks, Ming Gao, Ee Peng Lim, David Lo
David LO
The robustness of a network is determined by how well its vertices are connected to one another so as to keep the network strong and sustainable. As the network evolves its robustness changes and may reveal events as well as periodic trend patterns that affect the interactions among users in the network. In this paper, we develop R-energy as a new measure of network robustness based on the spectral analysis of normalized Laplacian matrix. R-energy can cope with disconnected networks, and is efficient to compute with a time complexity of O (jV j + jEj) where V and E are …
Extended Comprehensive Study Of Association Measures For Fault Localization, Lucia Lucia, David Lo, Lingxiao Jiang, Ferdian Thung, Aditya Budi
Extended Comprehensive Study Of Association Measures For Fault Localization, Lucia Lucia, David Lo, Lingxiao Jiang, Ferdian Thung, Aditya Budi
David LO
Spectrum-based fault localization is a promising approach to automatically locate root causes of failures quickly. Two well-known spectrum-based fault localization techniques, Tarantula and Ochiai, measure how likely a program element is a root cause of failures based on profiles of correct and failed program executions. These techniques are conceptually similar to association measures that have been proposed in statistics, data mining, and have been utilized to quantify the relationship strength between two variables of interest (e.g., the use of a medicine and the cure rate of a disease). In this paper, we view fault localization as a measurement of the …
Finding Relevant Answers In Software Forums, Swapna Gottopati, David Lo, Jing Jiang
Finding Relevant Answers In Software Forums, Swapna Gottopati, David Lo, Jing Jiang
David LO
Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containing relevant keywords. However, in software forums, often there are many threads containing similar keywords where each thread could contain a lot of posts as many as 1,000 or more. Manually finding relevant answers from these long threads is a painstaking task to …
Automated Detection Of Likely Design Flaws In Layered Architectures, Aditya Budi, - Lucia, David Lo, Lingxiao Jiang, Shaowei Wang
Automated Detection Of Likely Design Flaws In Layered Architectures, Aditya Budi, - Lucia, David Lo, Lingxiao Jiang, Shaowei Wang
David LO
Layered architecture prescribes a good principle for separating concerns to make systems more maintainable. One example of such layered architectures is the separation of classes into three groups: Boundary, Control, and Entity, which are referred to as the three analysis class stereotypes in UML. Classes of different stereotypes are interacting with one another, when properly designed, the overall interaction would be maintainable, flexible, and robust. On the other hand, poor design would result in less maintainable system that is prone to errors. In many software projects, the stereotypes of classes are often missing, thus detection of design flaws becomes non-trivial. …
Mining Antagonistic Communities From Social Networks, Kuan Zhang, David Lo, Ee Peng Lim
Mining Antagonistic Communities From Social Networks, Kuan Zhang, David Lo, Ee Peng Lim
David LO
During social interactions in a community, there are often sub-communities that behave in opposite manner. These antagonistic sub-communities could represent groups of people with opposite tastes, factions within a community distrusting one another, etc. Taking as input a set of interactions within a community, we develop a novel pattern mining approach that extracts for a set of antagonistic sub-communities. In particular, based on a set of user specified thresholds, we extract a set of pairs of sub-communities that behave in opposite ways with one another. To prevent a blow up in these set of pairs, we focus on extracting a …
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
David LO
We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “T-OLAP”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these …
Comprehensive Evaluation Of Association Measures For Fault Localization, Lucia Lucia, David Lo, Lingxiao Jiang, Aditya Budi
Comprehensive Evaluation Of Association Measures For Fault Localization, Lucia Lucia, David Lo, Lingxiao Jiang, Aditya Budi
David LO
In statistics and data mining communities, there have been many measures proposed to gauge the strength of association between two variables of interest, such as odds ratio, confidence, Yule-Y, Yule-Q, Kappa, and gini index. These association measures have been used in various domains, for example, to evaluate whether a particular medical practice is associated positively to a cure of a disease or whether a particular marketing strategy is associated positively to an increase in revenue, etc. This paper models the problem of locating faults as association between the execution or non-execution of particular program elements with failures. There have been …
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
David LO
Link structures are important patterns one looks out for when modeling and analyzing social networks. In this paper, we propose the task of mining interesting Link Formation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns capture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of existing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to using a support-confidence framework …