30,730 research outputs found
Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market
The original research question here is given by marketers in general, i.e.,
how to explain the changes in the desired timescale of the market. Tangled
String, a sequence visualization tool based on the metaphor where contexts in a
sequence are compared to tangled pills in a string, is here extended and
diverted to detecting stocks that trigger changes in the market and to
explaining the scenario of contextual shifts in the market. Here, the
sequential data on the stocks of top 10 weekly increase rates in the First
Section of the Tokyo Stock Exchange for 12 years are visualized by Tangled
String. The changing in the prices of stocks is a mixture of various timescales
and can be explained in the time-scale set as desired by using TS. Also, it is
found that the change points found by TS coincided by high precision with the
real changes in each stock price. As TS has been created from the data-driven
innovation platform called Innovators Marketplace on Data Jackets and is
extended to satisfy data users, this paper is as evidence of the contribution
of the market of data to data-driven innovations.Comment: 16 pages and 7 figures. The author started to write this paper as an
extension of the paper [20] in the reference list, but the content came to be
changed substantially, not by only minor extension but to a new pape
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
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The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge
From Social Data Mining to Forecasting Socio-Economic Crisis
Socio-economic data mining has a great potential in terms of gaining a better
understanding of problems that our economy and society are facing, such as
financial instability, shortages of resources, or conflicts. Without
large-scale data mining, progress in these areas seems hard or impossible.
Therefore, a suitable, distributed data mining infrastructure and research
centers should be built in Europe. It also appears appropriate to build a
network of Crisis Observatories. They can be imagined as laboratories devoted
to the gathering and processing of enormous volumes of data on both natural
systems such as the Earth and its ecosystem, as well as on human
techno-socio-economic systems, so as to gain early warnings of impending
events. Reality mining provides the chance to adapt more quickly and more
accurately to changing situations. Further opportunities arise by individually
customized services, which however should be provided in a privacy-respecting
way. This requires the development of novel ICT (such as a self- organizing
Web), but most likely new legal regulations and suitable institutions as well.
As long as such regulations are lacking on a world-wide scale, it is in the
public interest that scientists explore what can be done with the huge data
available. Big data do have the potential to change or even threaten democratic
societies. The same applies to sudden and large-scale failures of ICT systems.
Therefore, dealing with data must be done with a large degree of responsibility
and care. Self-interests of individuals, companies or institutions have limits,
where the public interest is affected, and public interest is not a sufficient
justification to violate human rights of individuals. Privacy is a high good,
as confidentiality is, and damaging it would have serious side effects for
society.Comment: 65 pages, 1 figure, Visioneer White Paper, see
http://www.visioneer.ethz.c
Bank Networks from Text: Interrelations, Centrality and Determinants
In the wake of the still ongoing global financial crisis, bank
interdependencies have come into focus in trying to assess linkages among banks
and systemic risk. To date, such analysis has largely been based on numerical
data. By contrast, this study attempts to gain further insight into bank
interconnections by tapping into financial discourse. We present a
text-to-network process, which has its basis in co-occurrences of bank names
and can be analyzed quantitatively and visualized. To quantify bank importance,
we propose an information centrality measure to rank and assess trends of bank
centrality in discussion. For qualitative assessment of bank networks, we put
forward a visual, interactive interface for better illustrating network
structures. We illustrate the text-based approach on European Large and Complex
Banking Groups (LCBGs) during the ongoing financial crisis by quantifying bank
interrelations and centrality from discussion in 3M news articles, spanning
2007Q1 to 2014Q3.Comment: Quantitative Finance, forthcoming in 201
MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants
Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest
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