14543078
Resource description framework: metadata and its applications
Universality, the property of the Web that makes it the largest data and information source in the world, is also the property behind the lack of a uniform organization scheme that would allow easy access to data and information. A semantic web, wherein different applications and Web sites can exchange information and hence exploit Web data and information to their full potential, requires the information about Web resources to be represented in a detailed and structured manner. Resource Description Framework (RDF), an effort in this direction supported by the World Wide Web Consortium, provides a means for the description of metadata which is a necessity for the next generation of interoperable Web applications. The success of RDF and the semantic web will depend on (1) the development of applications that prove the applicability of the concept, (2) the availability of application interfaces which enable the development of such applications, and (3) databases and inference systems that exploit RDF to identify and locate most relevant Web resources. In addition, many practical issues, such as security, ease of use, and compatibility, will be crucial in the success of RDF. This survey aims at providing a glimpse at the past, present, and future of this upcoming technology and highlights why we believe that the next generation of the Web will be more organized, informative, searchable, accessible, and, most importantly, useful. It is expected that knowledge discovery and data mining can benefit from RDF and the Semantic Web.
7193073
A selective sampling approach to active feature selection
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. Traditional feature selection methods resort to random sampling in dealing with data sets with a huge number of instances. In this paper, we introduce the concept of active feature selection, and investigate a selective sampling approach to active feature selection in a filter model setting. We present a formalism of selective sampling based on data variance, and apply it to a widely used feature selection algorithm Relief. Further, we show how it realizes active feature selection and reduces the required number of training instances to achieve time savings without performance deterioration. We design objective evaluation measures of performance, conduct extensive experiments using both synthetic and benchmark data sets, and observe consistent and significant improvement. We suggest some further work based on our study and experiments.
5365583
NeuroRule: A Connectionist Approach to Data Mining
Classification, which involves finding rules that partition a given da.ta set into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules for large databases are mainly decision tree based symbolic learning methods. The connectionist approach based on neura.l networks has been thought not well suited for data mining. One of the major reasons cited is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation by humans. This paper examines this issue. With our newly developed algorithms, rules which are similar to, or more concise than those generated by the symbolic methods can be extracted from the neural networks. The data mining process using neural networks with the emphasis on rule extraction is described. ExperimenM results and comparison with previously published works are presented.
8955639
An Empirical Study of Building Compact Ensembles
Ensemble methods can achieve excellent performance relying on member classifiers’ accuracy and diversity. We conduct an empirical study of the relationship of ensemble sizes with ensemble accuracy and diversity, respectively. Experiments with benchmark data sets show that it is feasible to keep a small ensemble while maintaining accuracy and diversity similar to those of a full ensemble. We propose a heuristic method that can effectively select member classifiers to form a compact ensemble. The idea of compact ensembles is motivated to use them for effective active learning in tasks of classification of unlabeled data.
8038499
Workshop Report
This is a report on a workshop held at Cornell University on Ma rch 30 – April 1, 2015. The workshop was held to honor Prof. Donald Farley on the occasio n of his 80th birthday (belatedly) and also to consider the future of radio and space physics. Ab out 60 scientists representing institutions in North America, South America, and Europe attende d. Participants included some of the most senior scientists in the field, mid-career scientists, early-career scientists, and students. Along with radio scientists, the workshop was attended by NASA inv estigators, theorists, modelers, and a representative from NSF. The focus of the workshop was the state of the AGS facilities. The first day of the workshop was retrospective in nature, and the second day considered cont emporary research being performed with the facilities. The third day focused on discussions about t he future. The discussions were vigorous and broad and extended to the vitality of not just the facilit ies but to geospace and heliospheric science generally. A recurring theme of the workshop was the fact that incoheren t scatter remains, by far, the most accurate and precise technique for studying the upper atmos phere, the standard by which other experimental techniques, ground-based and in situ, are valid ated and calibrated. Incoherent scatter experiments yield unambiguous estimates of the most import ant state parameters in the upper atmosphere with fine spatio-temporal resolution and offer the possibility of measurements with longterm continuity. Only sounding rockets make measurements w ith superior resolution, but sounding rockets are most often launched nearby incoherent scatter r dars since the latter provide critical experimental context. The incisive nature of incoherent scatter radar measuremen ts will be crucial for resolving fundamental problems in geospace physics. For example, the E region/ lower thermosphere is a complicated region of geospace that remains poorly understood . This is the region where turbulence transitions to laminar flow, where molecular oxygen is disso ciated, where ionization becomes abundant, where ions are unmagnetized whereas electrons are mag netized, and where strong neutral shears exist and electric currents flow. The dynamo fields whi ch drive the ionosphere are generated here and modulated by neutral waves in a complicated way whic h couples the upper atmosphere to the lower atmosphere. Neutral atmospheric waves break he re, but which waves penetrate and where the energy goes is unclear. Sporadic ionization layer s which have grave influence on radio propagation exist here, but their generation mechanism and the influence of the underlying neutral dynamics is not understood. Ground-based remote sensing us i our best experimental tool will be vital in elucidating the processes at work here. Much of the discussion concerned the dichotomy between basi c and applied research at the facilities, the two axes that define Pasteur’s quadrant. Examp les of basic research presented at the workshop included observations of heretofore unseen waves nd turbulence in the lower thermosphere made using broadband measurements of the full incohe rent scatter spectrum at Arecibo and HF interferometry at Jicamarca, observations of extraordi narily high-altitude irregularities over Jicamarca, and methods for inferring high-definition convect ion and current patterns with the PFISR. Applied research with the facilities includes long-durati on, 24-7 observations of basic state parameters used to drive global space-weather models.
11098583
Neighborhood unions and cyclability of graphs
A graph G is said to be cyclable if for each orientation G of G, there exists a set S of vertices such that reversing all the arcs of G with one end in S results in a hamiltonian digraph. Let G be a 3-connected graph of order n ≥ 36. In this paper, we show that if for any three independent vertices x1, x2 and x3, |N(x1) ∪ N(x2)| + |N(x2) ∪ N(x3)| + |N(x3) ∪ N(x1)| ≥ 2n+1, then G is cyclable.
207156949
Design and implementation of a single system image operating system for ad hoc networks
In this paper, we describe the design and implementation of a distributed operating system for ad hoc networks. Our system simplifies the programming of ad hoc networks and extends total system lifetime by making the entire network appear as a single virtual machine. It automatically and transparently partitions applications into components and dynamically finds them a placement on nodes within the network to reduce energy consumption and to increase system longevity. This paper describes our programming model, outlines the design and implementation of our system and examines the energy efficiency of our approach through extensive simulations as well as validation of a deployment on a physical testbed. We evaluate practical, power-aware, general-purpose algorithms for component placement and migration, and demonstrate that they can significantly increase system longevity by effectively distributing energy consumption and avoiding hotspots.
2931442
G-ToPSS: fast filtering of graph-based metadata
RDF is increasingly being used to represent metadata. RDF Site Summary (RSS) is an application of RDF on the Web that has considerably grown in popularity. However, the way RSS systems operate today does not scale well. In this paper we introduce G-ToPSS, a scalable publish/subscribe system for selective information dissemination. G-ToPSS is particularly well suited for applications that deal with large-volume content distribution from diverse sources. RSS is an instance of the content distribution problem. G-ToPSS allows use of ontology as a way to provide additional information about the data. Furthermore, in this paper we show how G-ToPSS can support RDFS class taxonomies. We have implemented and experimentally evaluated G-ToPSS and we provide results in the paper demonstrating its scalability compared to alternatives.
52857898
Schema Discovery for Semistructured Data
To formulate a meaningful query on semistructured data, such as on the Web, that matches some of the source's structure, we need first to discover something about how the information is represented in the source. This is referred to as schema discovery and was considered for a single object recently. In the case of multiple objects, the task of schema discovery is to identify typical structuring information of those objects as a whole. We motivate the schema discovery in this general setting and propose a framework and algorithm for it. We apply the framework to a real Web database, the Internet Movies Database, to discover typical schema of most voted movies.