Computing Exact Skyline Probabilities for Uncertain Databases
With the rapid increase in the amount of uncertain data available, probabilistic skyline computation on uncertain databases has become an important research topic. Previous work on probabilistic skyline computation, however, only identifies those ...
Constructing a New-Style Conceptual Model of Brain Data for Systematic Brain Informatics
The development of brain science has led to a vast increase of brain data. To meet requirements of a systematic methodology of Brain Informatics (BI), this paper proposes a new conceptual model of brain data, namely Data-Brain, which explicitly ...
Cost-Aware Rank Join with Random and Sorted Access
In this paper, we address the problem of joining ranked results produced by two or more services on the web. We consider services endowed with two kinds of access that are often available: 1) sorted access, which returns tuples sorted by score; 2) ...
Discovering the Most Influential Sites over Uncertain Data: A Rank-Based Approach
With the rapidly increasing availability of uncertain data in many important applications such as location-based services, sensor monitoring, and biological information management systems, uncertainty-aware query processing has received a significant ...
Efficient Mining of Frequent Item Sets on Large Uncertain Databases
The data handled in emerging applications like location-based services, sensor monitoring systems, and data integration, are often inexact in nature. In this paper, we study the important problem of extracting frequent item sets from a large uncertain ...
Hybrid Generative/Discriminative Approaches for Proportional Data Modeling and Classification
The work proposed in this paper is motivated by the need to develop powerful models and approaches to classify and learn proportional data. Indeed, an abundance of interesting data in several applications occur naturally in this form. Our goal is to ...
Joint Optimization of Index Freshness and Coverage in Real-Time Search Engines
Real-time search engines are increasingly indexing web content using data streams, since a number of web sources including news and social media sites are now delivering up-to-date information via streams. Accordingly, it is a crucial challenge for a ...
Mining Bucket Order-Preserving SubMatrices in Gene Expression Data
The Order-Preserving SubMatrices (OPSMs) are employed to discover significant biological associations between genes and experiment conditions. Herein, we propose a new relaxed OPSM model by considering the linearity relaxation, which is called the ...
Network Similarity Decomposition (NSD): A Fast and Scalable Approach to Network Alignment
As graph-structured data sets become commonplace, there is increasing need for efficient ways of analyzing such data sets. These analyses include conservation, alignment, differentiation, and discrimination, among others. When defined on general graphs, ...
Processing and Evaluating Partial Tree Pattern Queries on XML Data
XML query languages typically allow the specification of structural patterns using XPath. Usually, these structural patterns are in the form of trees (Tree-Pattern Queries—TPQs). Finding the occurrences of such patterns in an XML tree is a key operation ...
Query Representation through Lexical Association for Information Retrieval
A user query for information retrieval (IR) applications may not contain the most appropriate terms (words) as actually intended by the user. This is usually referred to as the term mismatch problem and is a crucial research issue in IR. Using the ...
Querying Uncertain Minimum in Wireless Sensor Networks
In this paper, we introduce two types of probabilistic aggregation queries, namely, Probabilistic Minimum Value Queries (PMVQ)s and Probabilistic Minimum Node Queries (PMNQ)s. A PMVQ determines possible minimum values among all imprecise sensed data, ...
Subontology Extraction Using Hyponym and Hypernym Closure on is-a Directed Acyclic Graphs
Ontologies are successfully used as semantic guides when navigating through the huge and ever increasing quantity of digital documents. Nevertheless, the size of numerous domain ontologies tends to grow beyond the human capacity to grasp information. ...
