22510987
Situation-aware personalized information retrieval for mobile Internet
Recent rapid advances in Internet-based information systems and handheld devices make it possible for users to retrieve information anytime and anywhere. Existing information retrieval (IR) techniques usually require the users to spend much effort to continuously refine their queries to obtain the results they want. Since each refinement is an interaction between a user and information systems, the larger the number of interactions, the more communication overhead and energy consumption are introduced. Due to the severe resource constraints of handheld devices, it is necessary to have an IR technique that can efficiently retrieve information for the user with only one or two interactions. In this paper, an efficient IR technique for mobile Internet by combining situation-based adaptation and profile-based personalization is presented.
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.
8367210
Sensor Sequence Modeling for Driving
Intelligent systems in automobiles need to be aware of the driving and driver context. Available sensor data stream has to be modeled and monitored in order to do so. Currently there exist no building blocks for hierarchical modeling of driving. By semi-supervised segmentation such building blocks can be discovered. We call them drivemes in analogy to phonemes. More parsimonious modeling of driving becomes now possible.
16386059
A Monotonic Measure for Optimal Feature Selection
Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distancebased measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification.
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.
46794790
Analysis of Hidden Representations by Greedy Clustering
The hidden layer of backpropagation neural networks (NNs) holds the key to the networks' success in solving pattern classification problems. The units in the hidden layer encapsulate the network's internal representations of the outside world described by the input data. this paper, the hidden representations of trained networks are investigated by means simple greedy clustering algorithm. This clustering algorithm is applied to networks have been trained to solve well-known problems: the monks problems, the 5-bit problem and the contiguity problem. The results from applying the algorithm to problems with known concepts provide us with a better understanding of NN learning. These also explain why NNs achieve higher predictive accuracy than that of decision-tree methods. The results of this study can be readily applied to rule extraction from Production rules are extracted for the parity and the monks problems, as well as benchmark data set: Pima Indian diabetes diagnosis. The extracted rules from the Indi...
14126485
Efficient and scalable filtering of graph-based metadata
RDF Site Summaries constitute an application of RDF on the Web that has considerably grown in popularity. However, the way RSS systems operate today limits their scalability. Current RSS feed arregators follow a pull-based architecture model, which is not going to scale with the increasing number of RSS feeds becoming available on the Web. In this paper, we introduce G-ToPSS, a scalable publish/subscribe system for selective information dissemination. G-ToPSS only sends newly updated information to the interested user and follows a push-based architecture model. G-ToPSS is particularly well suited for applications that deal with large-volume content distribution from diverse sources. G-ToPSS allows use of an ontology as a way to provide additional information about the data disseminated. We have implemented and experimentally evaluated G-ToPSS and we provide results demonstrating its scalability compared to alternative approaches. In addition, we describe an application of G-ToPSS and RSS to a Web-based content management system that provides an expressive, efficient, and convenient update notification dissemination system.
6684027
iProLINK: an integrated protein resource for literature mining
The exponential growth of large-scale molecular sequence data and of the PubMed scientific literature has prompted active research in biological literature mining and information extraction to facilitate genome/proteome annotation and improve the quality of biological databases. Motivated by the promise of text mining methodologies, but at the same time, the lack of adequate curated data for training and benchmarking, the Protein Information Resource (PIR) has developed a resource for protein literature mining--iProLINK (integrated Protein Literature INformation and Knowledge). As PIR focuses its effort on the curation of the UniProt protein sequence database, the goal of iProLINK is to provide curated data sources that can be utilized for text mining research in the areas of bibliography mapping, annotation extraction, protein named entity recognition, and protein ontology development. The data sources for bibliography mapping and annotation extraction include mapped citations (PubMed ID to protein entry and feature line mapping) and annotation-tagged literature corpora. The latter includes several hundred abstracts and full-text articles tagged with experimentally validated post-translational modifications (PTMs) annotated in the PIR protein sequence database. The data sources for entity recognition and ontology development include a protein name dictionary, word token dictionaries, protein name-tagged literature corpora along with tagging guidelines, as well as a protein ontology based on PIRSF protein family names. iProLINK is freely accessible at http://pir.georgetown.edu/iprolink, with hypertext links for all downloadable files.
208013251
Modeling the Effect of Size on Defect Proneness for Open-Source Software
Quality is becoming increasingly important with the continuous adoption of open-source software. Previous research has found that there is generally a positive relationship between module size and defect proneness. Therefore, in open-source software development, it is important to monitor module size and understand its impact on defect proneness. However, traditional approaches to quality modeling, which measure specific system snapshots and obtain future defect counts, are not well suited because open-source modules usually evolve and their size changes over time. In this study, we used Cox proportional hazards modeling with recurrent events to study the effect of class size on defect-proneness in the Mozilla product. We found that the effect of size was significant, and we quantified this effect on defect proneness.
893749
A Distributed and Efficient Flooding Scheme Using 1-Hop Information in Mobile Ad Hoc Networks
Flooding is one of the most fundamental operations in mobile ad hoc networks. Traditional implementation of flooding suffers from the problems of excessive redundancy of messages, resource contention, and signal collision. This causes high protocol overhead and interference with the existing traffic in the networks. Some efficient flooding algorithms were proposed to avoid these problems. However, these algorithms either perform poorly in reducing redundant transmissions or require each node to maintain 2-hop (or more) neighbors information. In the paper, we study the sufficient and necessary condition of 100 percent deliverability for flooding schemes that are based on only 1-hop neighbors information. We further propose an efficient flooding algorithm that achieves the local optimality in two senses: 1) the number of forwarding nodes in each step is minimal and 2) the time complexity for computing forwarding nodes is the lowest, which is O(nlogn), where n is the number of neighbors of a node. Extensive simulations have been conducted and simulation results have shown the excellent performance of our algorithm
38681572
Programmatic semantics for natural language interfaces
An important way of making interfaces usable by non-expert users is to enable the use of natural language input, as in natural language query interfaces to databases, or MUDs and MOOs. When the subject matter is about procedures, however, we have discovered that interfaces can take advantage of what we call Programmatic Semantics, procedural relations that can be inferred from the linguistic structure. Roughly, nouns can be interpreted as data structures; verbs are functions; adjectives are properties. Some linguistic forms imply conditionals, loops, and recursive structures.We illustrate the principles of Programmatic Semantics with a description of Metafor, a "brainstorming" editor for programs, analogous to an outlining tool for prose writing. Metafor interactively converts English sentences to partially specified program code, to be used as "scaffolding" for a more detailed program. A user study showed that Metafor is capable of capturing enough Programmatic Semantics to facilitate non-programming users and beginners' conceptualization of programming problems.