The document outlines the plans for a PhD research project on enhancing semantic interoperability among spreadsheets. The research will build upon a previous master's degree which identified construction patterns in spreadsheets and linked labels to ontologies within a single domain. The PhD plans to address limitations of the previous work by considering multiple domains, developing a model to relate elements across spreadsheets, and linking spreadsheet structure to ontologies at the concept level. Key research questions involve defining when spreadsheets share the same purpose, canonical representations among similar spreadsheets, and using representations to predict spreadsheet purpose and domain. The goal is achieving semantic interoperability across spreadsheets.
Object Analysis and Design has emerged as the most practiced method for analysis and design of information system. An object is any thing of interest in the real world that is being modeled.
For more such innovative content on management studies, join WeSchool PGDM-DLP Program: http://bit.ly/ZEcPAc
Studying Public Medical Images from Open Access Literature and Social Networks for Model Training and Knowledge Extraction
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Object Analysis and Design has emerged as the most practiced method for analysis and design of information system. An object is any thing of interest in the real world that is being modeled.
For more such innovative content on management studies, join WeSchool PGDM-DLP Program: http://bit.ly/ZEcPAc
Studying Public Medical Images from Open Access Literature and Social Networks for Model Training and Knowledge Extraction
Henning Müller, Vincent Andrearczyk, Oscar Jimenez, Anjani Dhrangadhariya
How to conduct systematic literature reviewKashif Hussain
The slides show how to conduct systematic literature review (SLR) in any field of research. It is highly important that any SLR should ultimately highlight potential future directions and research gaps so that prospect researchers may focus on those particular areas.
Systematic Literature Reviews and Systematic Mapping Studiesalessio_ferrari
Lecture slides on Systematic Literature Reviews and Systematic Mapping Studies in software engineering. It describes the different steps, discusses differences between the two methods, and gives guidelines on how to conduct these types of study.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
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Project repositories are a central asset in software development, as they preserve the technical knowledge gathered in past development activities. However, locating relevant information in a vast project repository is problematic, because it requires manually tagging projects with accurate metadata, an activity which is time consuming and prone to errors and omissions. This paper investigates the use of classical Information Retrieval techniques for easing the discovery of useful information from past projects. Differently from approaches based on textual search over the source code of applications or on querying structured metadata, we propose to index and search the models of applications, which are available in companies applying Model-Driven Engineering practices. We contrast alternative index structures and result presentations, and evaluate a prototype implementation on real-world experimental data.
Towards Ontology Development Based on Relational Databaseijbuiiir1
Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an automated method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data sources are put forward. Finally, ontology for �automated building of ontology from relational data sources� has been implemented
We are need to obtain information from several local or external sources, Each source may be built in different ways, so we will face many various conflicts in the meaning or structure and other conflicts. We'll see also examples show why need data integration .
A (vintage) presentation about a database system for the study of gene expression data. Including distributed metadata annotation and some interactive analytics. Some ideas are still actual today.
Integrating research indicators for use in the repositories infrastructure petrknoth
The current repository infrastructure, which consists of thousands of repositories, does not make effective use of research indicators largely exploited by commercial players in the area. Research indicators, including citation counts and Mendeley reader counts, enable the development and improvement of functionality researchers use on a daily basis. For example, they make it possible to increase the performance in information retrieval and recommendation tasks and serve as an enabler for the development of research analytics & metrics functionality, such as the analysis of research trends or collaboration networks. We believe that there is a strong case for making a better use of these indicators within the repositories infrastructure to improve the functionality of services users rely on.
Amit Sheth with TK Prasad, "Semantic Technologies for Big Science and Astrophysics", Invited Plenary Presentation, at Earthcube Solar-Terrestrial End-User Workshop, NJIT, Newark, NJ, August 13, 2014.
Like many other fields of Big Science, Astrophysics and Solar Physics deal with the challenges of Big Data, including Volume, Variety, Velocity, and Veracity. There is already significant work on handling volume related challenges, including the use of high performance computing. In this talk, we will mainly focus on other challenges from the perspective of collaborative sharing and reuse of broad variety of data created by multiple stakeholders, large and small, along with tools that offer semantic variants of search, browsing, integration and discovery capabilities. We will borrow examples of tools and capabilities from state of the art work in supporting physicists (including astrophysicists) [1], life sciences [2], material sciences [3], and describe the role of semantics and semantic technologies that make these capabilities possible or easier to realize. This applied and practice oriented talk will complement more vision oriented counterparts [4].
[1] Science Web-based Interactive Semantic Environment: http://sciencewise.info/
[2] NCBO Bioportal: http://bioportal.bioontology.org/ , Kno.e.sis’s work on Semantic Web for Healthcare and Life Sciences: http://knoesis.org/amit/hcls
[3] MaterialWays (a Materials Genome Initiative related project): http://wiki.knoesis.org/index.php/MaterialWays
[4] From Big Data to Smart Data: http://wiki.knoesis.org/index.php/Smart_Data
Zdravković Milan, Trajanović Miroslav. Semantic interoperability of Supply Ch...Milan Zdravković
Presentation from the 1st Workshop on Future Internet Enterprise Systems - FINES 2010: Ontologies and Interoperability, made at 10.11.2010 in Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems
How to conduct systematic literature reviewKashif Hussain
The slides show how to conduct systematic literature review (SLR) in any field of research. It is highly important that any SLR should ultimately highlight potential future directions and research gaps so that prospect researchers may focus on those particular areas.
Systematic Literature Reviews and Systematic Mapping Studiesalessio_ferrari
Lecture slides on Systematic Literature Reviews and Systematic Mapping Studies in software engineering. It describes the different steps, discusses differences between the two methods, and gives guidelines on how to conduct these types of study.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
Searching Repositories of Web Application ModelsMarco Brambilla
Project repositories are a central asset in software development, as they preserve the technical knowledge gathered in past development activities. However, locating relevant information in a vast project repository is problematic, because it requires manually tagging projects with accurate metadata, an activity which is time consuming and prone to errors and omissions. This paper investigates the use of classical Information Retrieval techniques for easing the discovery of useful information from past projects. Differently from approaches based on textual search over the source code of applications or on querying structured metadata, we propose to index and search the models of applications, which are available in companies applying Model-Driven Engineering practices. We contrast alternative index structures and result presentations, and evaluate a prototype implementation on real-world experimental data.
Towards Ontology Development Based on Relational Databaseijbuiiir1
Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an automated method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data sources are put forward. Finally, ontology for �automated building of ontology from relational data sources� has been implemented
We are need to obtain information from several local or external sources, Each source may be built in different ways, so we will face many various conflicts in the meaning or structure and other conflicts. We'll see also examples show why need data integration .
A (vintage) presentation about a database system for the study of gene expression data. Including distributed metadata annotation and some interactive analytics. Some ideas are still actual today.
Integrating research indicators for use in the repositories infrastructure petrknoth
The current repository infrastructure, which consists of thousands of repositories, does not make effective use of research indicators largely exploited by commercial players in the area. Research indicators, including citation counts and Mendeley reader counts, enable the development and improvement of functionality researchers use on a daily basis. For example, they make it possible to increase the performance in information retrieval and recommendation tasks and serve as an enabler for the development of research analytics & metrics functionality, such as the analysis of research trends or collaboration networks. We believe that there is a strong case for making a better use of these indicators within the repositories infrastructure to improve the functionality of services users rely on.
Amit Sheth with TK Prasad, "Semantic Technologies for Big Science and Astrophysics", Invited Plenary Presentation, at Earthcube Solar-Terrestrial End-User Workshop, NJIT, Newark, NJ, August 13, 2014.
Like many other fields of Big Science, Astrophysics and Solar Physics deal with the challenges of Big Data, including Volume, Variety, Velocity, and Veracity. There is already significant work on handling volume related challenges, including the use of high performance computing. In this talk, we will mainly focus on other challenges from the perspective of collaborative sharing and reuse of broad variety of data created by multiple stakeholders, large and small, along with tools that offer semantic variants of search, browsing, integration and discovery capabilities. We will borrow examples of tools and capabilities from state of the art work in supporting physicists (including astrophysicists) [1], life sciences [2], material sciences [3], and describe the role of semantics and semantic technologies that make these capabilities possible or easier to realize. This applied and practice oriented talk will complement more vision oriented counterparts [4].
[1] Science Web-based Interactive Semantic Environment: http://sciencewise.info/
[2] NCBO Bioportal: http://bioportal.bioontology.org/ , Kno.e.sis’s work on Semantic Web for Healthcare and Life Sciences: http://knoesis.org/amit/hcls
[3] MaterialWays (a Materials Genome Initiative related project): http://wiki.knoesis.org/index.php/MaterialWays
[4] From Big Data to Smart Data: http://wiki.knoesis.org/index.php/Smart_Data
Zdravković Milan, Trajanović Miroslav. Semantic interoperability of Supply Ch...Milan Zdravković
Presentation from the 1st Workshop on Future Internet Enterprise Systems - FINES 2010: Ontologies and Interoperability, made at 10.11.2010 in Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems
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use the Recommended Charts feature,
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consider how to add relevant data visualization elements (including data labels, background grids, axis labels, and titles) for a coherent and effective data visualization.
Also, participants will help co-build data visualizations from open-source and other datasets.
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http://sandymillin.wordpress.com/iateflwebinar2024
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Biological screening of herbal drugs: Introduction and Need for
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The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
13. Which elements must be
considered in this
interpretation process?
Unity Interpretation
14. Related Work
isolated label
(Han et al,. 2008) - RDF123: from spreadsheets to RDF, The Semantic Web. Lecture Notes in Computer Science, vol. 5318. Springer
(Langegger & Wolfram, 2009) - XLWrap Querying and Integrating Arbitrary Spreadsheets with SPARQL, The Semantic Web. Lecture
Notes in Computer Science, vol. 5823. Springer
15. Related Work
template
(Abraham & Erwig, 2006) - Inferring Templates from Spreadsheets, Proceedings of the International Conference on Software Engineering
16. Related Work
instances
(Zhao et al, 2010) - A spreadsheet system based on data semantic object, IEEE International Conference on Information Management and
Engineering
17. Related Work
isolated label associated to
linked data
(Syed et al., 2010) - Exploiting a Web of Semantic Data for Interpreting Tables, Proceedings of the Web Science Conference
18. Related Work
correlation of labels
associated to linked data
(Venetis et al., 2011) - Recovering Semantics of Tables on the Web, Proceedings of the VLDB Endowment
(Mulwad et al., 2010) - Using linked data to interpret tables, Proceedings of the International Workshop on Consuming Linked Data
19. Related Work
correlation between several
spreadsheet elements
associated to linked data
(Limaye, 2010) - Annotating and Searching Web Tables Using Entities, Proceedings of the VLDB Endowment
20. How far the system can
interpret, considering labels and
their correlations?
26. Research Strategy
1. To identify construction patterns followed by biologists
during the creation of these spreadsheets
2. To verify if these construction patterns could lead us to
recognition of the spreadsheet purpose
3. To achieve a semantic interoperability among these
spreadsheets
43. Architecture Evaluation
Automatic analysis of 11,150 spreadsheets
the system recognized 1,151 spreadsheets
806 spreadsheets were classified as catalogue
345 spreadsheets were classified as collection
Total: 748,459 records analyzed
*
44. Architecture Evaluation - Results
• Random subset of 1,203 spreadsheets was
selected to evaluate precision/recall
– Precision: 0.84
– Recall: 0.76
– Specificity: 0.95
*
46. Main Limitations● Single Domain
Specific spreadsheets (catalogue and
collection)
● Lack of a Model to represent
construction patterns
○ after, model for construction
patterns isolated for each other
● Linking labels to ontologies
○ not able to aggregate different
labels belonging to the same
concept
○ the ontology was selected by us, it
is not necessarily the best
representation for spreadsheets'
data
47. ● Single Domain
○ Specific spreadsheets (catalogue
and collection)
● Lack of a Model to represent
construction patterns
○ after, model for construction
patterns isolated for each other
● Linking labels to ontologies
○ not able to aggregate different
labels belonging to the same
concept
○ the ontology was selected by us, it
is not necessarily the best
representation for spreadsheets'
data
● Multiple Domains
● Model as an association
network
○ relates elements and
concepts of several
spreadsheets
● Linking spreadsheet structure
to ontologies
○ the link is made between
concepts
87. Research Questions
• When spreadsheets could be considered of the
same purpose?
• Is there a canonical representation among
spreadsheets of the same purpose?
• Is it possible to define a canonical representation
for a spreadsheet group
• Can this representation be used to predict
spreadsheets of a given purpose?
88. Acknowledgements
● Laboratory of Information Systems (LIS)
● UNICAMP
● FAPESP
● Microsoft Research FAPESP Virtual Institute
(NavScales project)
● CNPq (MuZOO Project and PRONEX-FAPESP)
● INCT in Web Science(CNPq 557.128/2009-9)
● CAPES