A Blocking Framework for Entity Resolution in Highly Heterogeneous Information Spaces
In the context of entity resolution (ER) in highly heterogeneous, noisy, user-generated entity collections, practically all block building methods employ redundancy to achieve high effectiveness. This practice, however, results in a high number of ...
A Learning Approach to SQL Query Results Ranking Using Skyline and Users' Current Navigational Behavior
Users often find that their queries against a database return too many answers, many of them irrelevant. A common solution is to rank the query results. The effectiveness of a ranking function depends on how well it captures users' preferences. However, ...
A Robust, Distortion Minimizing Technique for Watermarking Relational Databases Using Once-for-All Usability Constraints
Ownership protection on relational databases--shared with collaborators (or intended recipients)--demands developing a watermarking scheme that must be able to meet four challenges: 1) it should be robust against different types of attacks that an ...
Active Trace Clustering for Improved Process Discovery
Process discovery is the learning task that entails the construction of process models from event logs of information systems. Typically, these event logs are large data sets that contain the process executions by registering what activity has taken ...
Bridging Causal Relevance and Pattern Discriminability: Mining Emerging Patterns from High-Dimensional Data
It is a nontrivial task to build an accurate emerging pattern (EP) classifier from high-dimensional data because we inevitably face two challenges 1) how to efficiently extract a minimal set of strongly predictive EPs from an explosive number of ...
Disputant Relation-Based Classification for Contrasting Opposing Views of Contentious News Issues
Contentious news issues, such as the health care reform debate, draw much interest from the public; however, it is not simple for an ordinary user to search and contrast the opposing arguments and have a comprehensive understanding of the issues. ...
Effective Online Group Discovery in Trajectory Databases
GPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel ...
Efficient Keyword Search on Uncertain Graph Data
As a popular search mechanism, keyword search has been applied to retrieve useful data in documents, texts, graphs, and even relational databases. However, so far, there is no work on keyword search over uncertain graph data even though the uncertain ...
EnBay: A Novel Pattern-Based Bayesian Classifier
A promising approach to Bayesian classification is based on exploiting frequent patterns, i.e., patterns that frequently occur in the training data set, to estimate the Bayesian probability. Pattern-based Bayesian classification focuses on building and ...
Group Location Selection Queries over Uncertain Objects
Given a set of spatial objects, facilities can influence the objects located within their influence regions that are represented by circular disks with the same radius $(r)$. Our task is to select the minimum number of locations such that establishing a ...
Integrated Oversampling for Imbalanced Time Series Classification
This paper proposes a novel Integrated Oversampling (INOS) method that can handle highly imbalanced time series classification. We introduce an enhanced structure preserving oversampling (ESPO) technique and synergistically combine it with interpolation-...
Measuring Similarity Based on Link Information: A Comparative Study
Measuring similarity between objects is a fundamental task in domains such as data mining, information retrieval, and so on. Link-based similarity measures have attracted the attention of many researchers and have been widely applied in recent years. ...
On-Demand Snapshot: An Efficient Versioning File System for Phase-Change Memory
Versioning file systems are widely used in modern computer systems as they provide system recovery and old data access functions by retaining previous file system snapshots. However, existing versioning file systems do not perform well with the emerging ...
Online Seizure Prediction Using an Adaptive Learning Approach
Epilepsy is one of the most common neurological disorders, characterized by recurrent seizures. Being able to predict impending seizures could greatly improve the lives of patients with epilepsy. In this study, we propose a new adaptive learning ...
Signature-Based Detection of Notable Transitions in Numeric Data Streams
A major challenge in large-scale process monitoring is to recognize significant transitions in the process conditions and to distinguish them from random fluctuations that do not produce a notable change in the process dynamics. Such transitions should ...
Valid-Time Indeterminacy in Temporal Relational Databases: Semantics and Representations
Valid-time indeterminacy is "don't know when" indeterminacy, coping with cases in which one does not exactly know when a fact holds in the modeled reality. In this paper, we first propose a reference representation (data model and algebra) in which all ...
Dynamic Personalized Recommendation on Sparse Data
Recommendation techniques are very important in the fields of E-commerce and other web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized ...
Hierarchical Sampling for Multi-Instance Ensemble Learning
In this paper, we propose a Hierarchical Sampling-based Multi-Instance ensemble LEarning (HSMILE) method. Due to the unique multi-instance learning nature, a positive bag contains at least one positive instance whereas samples (instance and sample are ...