4,552 research outputs found
Dynamic Discovery of Type Classes and Relations in Semantic Web Data
The continuing development of Semantic Web technologies and the increasing
user adoption in the recent years have accelerated the progress incorporating
explicit semantics with data on the Web. With the rapidly growing RDF (Resource
Description Framework) data on the Semantic Web, processing large semantic
graph data have become more challenging. Constructing a summary graph structure
from the raw RDF can help obtain semantic type relations and reduce the
computational complexity for graph processing purposes. In this paper, we
addressed the problem of graph summarization in RDF graphs, and we proposed an
approach for building summary graph structures automatically from RDF graph
data. Moreover, we introduced a measure to help discover optimum class
dissimilarity thresholds and an effective method to discover the type classes
automatically. In future work, we plan to investigate further improvement
options on the scalability of the proposed method
Unsupervised Algorithms for Microarray Sample Stratification
The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.Peer reviewe
Multi-Source Spatial Entity Linkage
Besides the traditional cartographic data sources, spatial information can
also be derived from location-based sources. However, even though different
location-based sources refer to the same physical world, each one has only
partial coverage of the spatial entities, describe them with different
attributes, and sometimes provide contradicting information. Hence, we
introduce the spatial entity linkage problem, which finds which pairs of
spatial entities belong to the same physical spatial entity. Our proposed
solution (QuadSky) starts with a time-efficient spatial blocking technique
(QuadFlex), compares pairwise the spatial entities in the same block, ranks the
pairs using Pareto optimality with the SkyRank algorithm, and finally,
classifies the pairs with our novel SkyEx-* family of algorithms that yield
0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs
and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of
777,452 pairs. Moreover, we provide a theoretical guarantee and formalize the
SkyEx-FES algorithm that explores only 27% of the skylines without any loss in
F-measure. Furthermore, our fully unsupervised algorithm SkyEx-D approximates
the optimal result with an F-measure loss of just 0.01. Finally, QuadSky
provides the best trade-off between precision and recall, and the best
F-measure compared to the existing baselines and clustering techniques, and
approximates the results of supervised learning solutions
State of the art document clustering algorithms based on semantic similarity
The constant success of the Internet made the number of text documents in electronic forms increases hugely. The techniques to group these documents into meaningful clusters are becoming critical missions. The traditional clustering method was based on statistical features, and the clustering was done using a syntactic notion rather than semantically. However, these techniques resulted in un-similar data gathered in the same group due to polysemy and synonymy problems. The important solution to this issue is to document clustering based on semantic similarity, in which the documents are grouped according to the meaning and not keywords. In this research, eighty papers that use semantic similarity in different fields have been reviewed; forty of them that are using semantic similarity based on document clustering in seven recent years have been selected for a deep study, published between the years 2014 to 2020. A comprehensive literature review for all the selected papers is stated. Detailed research and comparison regarding their clustering algorithms, utilized tools, and methods of evaluation are given. This helps in the implementation and evaluation of the clustering of documents. The exposed research is used in the same direction when preparing the proposed research. Finally, an intensive discussion comparing the works is presented, and the result of our research is shown in figures
Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing
Analysis of what has come to be called “big data” presents a number of challenges as data continues to grow in size, complexity and heterogeneity. To help addresses these challenges, we study a pair of foundational issues in algorithmic stability (robustness and tuning), with application to clustering in high-throughput computational biology, and an issue in data cleansing (outlier detection), with application to pre-processing in streaming meteorological measurement. These issues highlight major ongoing research aspects of modern big data analytics. First, a new metric, robustness, is proposed in the setting of biological data clustering to measure an algorithm’s tendency to maintain output coherence over a range of parameter settings. It is well known that different algorithms tend to produce different clusters, and that the choice of algorithm is often driven by factors such as data size and type, similarity measure(s) employed, and the sort of clusters desired. Even within the context of a single algorithm, clusters often vary drastically depending on parameter settings. Empirical comparisons performed over a variety of algorithms and settings show highly differential performance on transcriptomic data and demonstrate that many popular methods actually perform poorly. Second, tuning strategies are studied for maximizing biological fidelity when using the well-known paraclique algorithm. Three initialization strategies are compared, using ontological enrichment as a proxy for cluster quality. Although extant paraclique codes begin by simply employing the first maximum clique found, results indicate that by generating all maximum cliques and then choosing one of highest average edge weight, one can produce a small but statistically significant expected improvement in overall cluster quality. Third, a novel outlier detection method is described that helps cleanse data by combining Pearson correlation coefficients, K-means clustering, and Singular Spectrum Analysis in a coherent framework that detects instrument failures and extreme weather events in Atmospheric Radiation Measurement sensor data. The framework is tested and found to produce more accurate results than do traditional approaches that rely on a hand-annotated database
StructMatrix: large-scale visualization of graphs by means of structure detection and dense matrices
Given a large-scale graph with millions of nodes and edges, how to reveal
macro patterns of interest, like cliques, bi-partite cores, stars, and chains?
Furthermore, how to visualize such patterns altogether getting insights from
the graph to support wise decision-making? Although there are many algorithmic
and visual techniques to analyze graphs, none of the existing approaches is
able to present the structural information of graphs at large-scale. Hence,
this paper describes StructMatrix, a methodology aimed at high-scalable visual
inspection of graph structures with the goal of revealing macro patterns of
interest. StructMatrix combines algorithmic structure detection and adjacency
matrix visualization to present cardinality, distribution, and relationship
features of the structures found in a given graph. We performed experiments in
real, large-scale graphs with up to one million nodes and millions of edges.
StructMatrix revealed that graphs of high relevance (e.g., Web, Wikipedia and
DBLP) have characterizations that reflect the nature of their corresponding
domains; our findings have not been seen in the literature so far. We expect
that our technique will bring deeper insights into large graph mining,
leveraging their use for decision making.Comment: To appear: 8 pages, paper to be published at the Fifth IEEE ICDM
Workshop on Data Mining in Networks, 2015 as Hugo Gualdron, Robson Cordeiro,
Jose Rodrigues (2015) StructMatrix: Large-scale visualization of graphs by
means of structure detection and dense matrices In: The Fifth IEEE ICDM
Workshop on Data Mining in Networks 1--8, IEE
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