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2014 | Buch

Geospatial Digital Dashboard for Exploratory Visual Analytics Kapitel lesen Erstes Kapitel lesen

Information Search, Integration, and Personalization

International Workshop, ISIP 2013, Bangkok, Thailand, September 16--18, 2013. Revised Selected Papers

herausgegeben von: Asanee Kawtrakul, Dominique Laurent, Nicolas Spyratos, Yuzuru Tanaka

Verlag: Springer International Publishing

Buchreihe : Communications in Computer and Information Science

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book constitutes the refereed post-proceedings of the International Workshop on Information Search, Integration and Personalization, ISIP 2013, held in Bangkok, Thailand, in September 2013. The 10 revised full papers presented were carefully reviewed and selected from 28 presentations. The papers are organized in topical sections on knowledge federation and integration; information discovery; recommendation systems and ontologies.

Inhaltsverzeichnis

Frontmatter

Knowledge Federation and Integration

Frontmatter
Geospatial Digital Dashboard for Exploratory Visual Analytics
Abstract
We present a system for visual data exploration, built using pluggable software components, which allows ad hoc combination of data from different sources (“data mash-up”). Interaction is done through “direct manipulation”, making it easy to use for domain experts that may not be data mining or computer experts. All visualized results can be interacted with, and selections or groupings using one visualization result are automatically reflected in all other views of the same data.
Jonas Sjöbergh, Yuzuru Tanaka
Authoring Composite Documents and Their Descriptions
Abstract
We present a method for describing composite documents based on the descriptions of their components. Our main objective is to assist authors of composite documents in selecting documents and their descriptions during the authoring process. We assume that a document description is a set of terms from a given taxonomy, such that no two terms in the set are comparable. We call such a description a “reduced description” and we show that the set of all reduced descriptions forms a complete lattice under an appropriate ordering. Based on this lattice we introduce the concept of “admissible description” and we argue that admissible descriptions are the only ones that describe composite documents in a useful and meaningful manner.
Nicolas Spyratos, Tsuyoshi Sugibuchi
Rewriting Aggregate Queries Using Functional Dependencies within the Cloud
Abstract
Since many years, companies and laboratories have had a pressing need for processing large amounts of data in areas such as astronomy, medicine or social networks. Cloud computing provides users with a virtually infinite amount of computing resources. Scaling up cloud performance can be usually achieved by using more numerous and/or more powerful nodes. However, this results in high costs as well as using more resources than necessary. In the area of databases, caching and query rewriting are two important ways to improve performance. This paper proposes rewriting rules for aggregate queries using semantic caching in the cloud. We have implemented our proposal in the Pig system and conducted experiments in a private cloud.
Romain Perriot, Laurent d’Orazio, Dominique Laurent, Nicolas Spyratos

Information Discovery

Frontmatter
Do Stock Analysts Make Good Recommendations: A Unified System for Analysts’ Performance Tracking and Ranking
Abstract
Stock analyst’s report is among of several important information sources for making investment decisions, as it contains relevant information about stocks as well as recommendation where investors should buy or sell the stock together with entry and exit strategies. Good analysts should often make trustworthy recommendations so that traders following them can make regularly profits from their advices. Nevertheless, identifying good analysts is not a trivial task especially when processed manually. Particularly, one has to collect and extract strategies from unstructured texts appearing in analyst reports, backtest such strategies with historical market data, and summarize backtested results by overall profits and losses. To address these problems, we propose a unified system which makes use of a combination of information integration and computational finance techniques to automate all these tasks. Our system performs considerably well in extracting recommendations from various analysts’ reports and provides new valuable information to traders. The system has been made available online as a mobile application for community use.
Chaiyakorn Yingsaeree, Anon Plangprasopchok, Paramet Tanwanont, Rattapoom Tuchinda
Towards Facilitating the Development of Monitoring Systems with Low-Cost Autonomous Mobile Robots
Abstract
This paper presents our ongoing work towards facilitating the development of monitoring systems with low cost autonomous mobile robots by unifying techniques from data mining and databases. Such a system is less invasive to privacy, more flexible to changes, and more focused in its observation. However, it is subject to major obstacles which are diverse targets, massive data, and huge management cost. We explain the base techniques of a discovery robot coupled with service-oriented declarative system. We sketch our applications on fall risk discovery and point out some challenges to be addressed.
Einoshin Suzuki, Yutaka Deguchi, Daisuke Takayama, Shigeru Takano, Vasile-Marian Scuturici, Jean-Marc Petit
On Skill Acquisition Support by Analogical Rule Abduction
Abstract
This paper describes our development of analogical abduction as an extension to our work on meta level abductive reasoning for rule abduction and predicate invention. Previously, we gave a set of axioms to state the object level causalities in terms of first-order-logic (FOL) clauses, which represent direct and indirect causalities with transitive rules. Here we extend our formalism of the meta level abductive reasoning, by adding rules to conduct analogical inference. We have applied our analogical abduction method to the problem of explaining the difficult cello playing techniques of spiccato and rapid cross strings of the bow movement. Our method has constructed persuasive analogical explanations about how to play them. We have used a model of forced vibration mechanics as the base world for spiccato, and the specification of the skeletal structure of the hand as the basis for the cross string bowing technique. We also applied analogical abduction to show the effectiveness of a metaphorical expression of “eating pancake on the sly” to achieve forte-piano dynamics, and successfully created an analogical explanation of how it works.
Koichi Furukawa, Keita Kinjo, Tomonobu Ozaki, Makoto Haraguchi
Mining Interesting Disjunctive Association Rules from Unfrequent Items
Abstract
In most approaches to mining association rules, interestingness relies on frequent items, i.e., rules are built using items that frequently occur in the transactions. However, in many cases, data sets contain unfrequent items that can reveal useful knowledge that most standard algorithms fail to mine. For example, if items are products, it might be that each of the products \(p_1\) and \(p_2\) does not sell very well (i.e., none of them appears frequently in the transactions) but, that selling products \(p_1\) or \(p_2\) is frequent (i.e., transactions containing \(p_1\) or \(p_2\) are frequent). Then, assuming that \(p_1\) and \(p_2\) are similar enough with respect to a given similarity measure, the set \(\{p_1, p_2\}\) can be considered for mining relevant rules of the form \(\{p_1, p_2\} \rightarrow \{p_3, p_4\}\) (assuming that \(p_3\) and \(p_4\) are unfrequent similar products such that \(\{p_3,p_4\}\) is frequent), meaning that most of customers buying \(p_1\) or \(p_2\), also buy \(p_3\) or \(p_4\). The goal of our work is to mine association rules of the form \(D_1 \rightarrow D_2\) such that \((i)\) \(D_1\) and \(D_2\) are disjoint homogeneous frequent itemsets made up with unfrequent items, and \((ii)\) the support and the confidence of the rule are respectively greater than or equal to given thresholds. The main contributions of this paper towards this goal are to set the formal definitions, properties and algorithms for mining such rules.
Ines Hilali, Tao-Yuan Jen, Dominique Laurent, Claudia Marinica, Sadok Ben Yahia

Recommendation Systems and Ontologies

Frontmatter
MA_THR: Multi-Agent Thai Herb Recommendation from Heterogeneous Data Sources
Abstract
Multi-Agent Thai Herb Recommendation system (MA_THR) recommends Thai herb treatments based on a personal patient profile. Thai herb information is collected from available heterogeneous data sources, such as outside databases and websites. The collected information is integrated into a main Thai herb ontology, which is used as a knowledge base of the system. In order to integrate each component into one solution, multi-agent architecture is designed and implemented. The overall system evaluation justified by three human experts gives 89 % precision and 94 % recall.
Ponrudee Netisopakul, Phakphoom Chainapaporn
Ontology Design Approaches for Development of an Excise Duty Recommender System
Abstract
Excise duty is a type of tax charged on certain products and services. Determining an excise product class can be a difficult task since the regulation written in legal language can be difficult for the users to interpret. Excise duty recommender system aims to simplify the users’ effort in product classification task and reduce errors in tax payment. This paper describes an initiative to develop excise product ontology to provide explicit and formal definitions of excise products and their classifications. It focuses on ontology design approaches for some excise products to support excise duty recommender system development. Two excise products were used as case studies: beverage and petroleum products. Steps in defining product mapping rules and developing the recommender system are described. A Semantic Web-based system architecture was adopted to enable future support for data sharing and reuse based on the Linked Data technology.
Marut Buranarach, Taneth Ruangrajitpakorn, Chutiporn Anutariya, Vilas Wuwongse
Earth Observation Data Interoperability Arrangement with Vocabulary Registry
Abstract
Standardization organizations are working for syntactic and schematic level of interoperability. At the same time, semantic interoperability should be considered as heterogeneous conditions and also very diversified with a large-volume data. The vocabulary registry has been developed and ontological information such as technical vocabularies for earth observation has been collected for data interoperability arrangement. This is a very challenging method for earth observation data interoperability because collaboration or cooperation with scientists of different disciplines is essential for common understanding. SKOS-editor is developed to register and update technical vocabularies as a part of the vocabulary registry, which promises to be a useful tool for users to handle SKOS format. In order to invite contributions from the user community, it is necessary to provide sophisticated and easy-to-use tools. Registered vocabularies supply the reference information required for earth observation data retrieval. We proposed data/metadata search with ontology such as technical vocabularies and visualization of relations among dataset to very large scale and various earth observation data.
Masahiko Nagai, Ashik Rajbhandari, Masafumi Ono, Ryosuke Shibaski
Backmatter
Metadaten
Titel
Information Search, Integration, and Personalization
herausgegeben von
Asanee Kawtrakul
Dominique Laurent
Nicolas Spyratos
Yuzuru Tanaka
Copyright-Jahr
2014
Electronic ISBN
978-3-319-08732-0
Print ISBN
978-3-319-08731-3
DOI
https://doi.org/10.1007/978-3-319-08732-0

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