6,895 research outputs found
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
Algorithmic and Statistical Perspectives on Large-Scale Data Analysis
In recent years, ideas from statistics and scientific computing have begun to
interact in increasingly sophisticated and fruitful ways with ideas from
computer science and the theory of algorithms to aid in the development of
improved worst-case algorithms that are useful for large-scale scientific and
Internet data analysis problems. In this chapter, I will describe two recent
examples---one having to do with selecting good columns or features from a (DNA
Single Nucleotide Polymorphism) data matrix, and the other having to do with
selecting good clusters or communities from a data graph (representing a social
or information network)---that drew on ideas from both areas and that may serve
as a model for exploiting complementary algorithmic and statistical
perspectives in order to solve applied large-scale data analysis problems.Comment: 33 pages. To appear in Uwe Naumann and Olaf Schenk, editors,
"Combinatorial Scientific Computing," Chapman and Hall/CRC Press, 201
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
Approximation contexts in addressing graph data structures
While the application of machine learning algorithms to practical problems has been expanded from fixed sized input data to sequences, trees or graphs input data, the composition of learning system has developed from a single model to integrated ones. Recent advances in graph based learning algorithms include: the SOMSD (Self Organizing Map for Structured Data), PMGraphSOM (Probability Measure Graph Self Organizing Map,GNN (Graph Neural Network) and GLSVM (Graph Laplacian Support Vector Machine). A main motivation of this thesis is to investigate if such algorithms, whether by themselves individually or modified, or in various combinations, would provide better performance over the more traditional artificial neural networks or kernel machine methods on some practical challenging problems. More succinctly, this thesis seeks to answer the main research question: when or under what conditions/contexts could graph based models be adjusted and tailored to be most efficacious in terms of predictive or classification performance on some challenging practical problems? There emerges a range of sub-questions including: how do we craft an effective neural learning system which can be an integration of several graph and non-graph based models? Integration of various graph based and non graph based kernel machine algorithms; enhancing the capability of the integrated model in working with challenging problems; tackling the problem of long term dependency issues which aggravate the performance of layer-wise graph based neural systems. This thesis will answer these questions.
Recent research on multiple staged learning models has demonstrated the efficacy of multiple layers of alternating unsupervised and supervised learning approaches. This underlies the very successful front-end feature extraction techniques in deep neural networks. However much exploration is still possible with the investigation of the number of layers required, and the types of unsupervised or supervised learning models which should be used. Such issues have not been considered so far, when the underlying input data structure is in the form of a graph. We will explore empirically the capabilities of models of increasing complexities, the combination of the unsupervised learning algorithms, SOM, or PMGraphSOM, with or without a cascade connection with a multilayer perceptron, and with or without being followed by multiple layers of GNN. Such studies explore the effects of including or ignoring context. A parallel study involving kernel machines with or without graph inputs has also been conducted empirically
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