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Secondpresentation
Secondpresentation
mdanielavillagrandesign
Presented at the 15th International Database Engineering & Applications Symposium (IDEAS 2011), September 21-23, 2011, Lisbon, Portugal.
SciQL, Bridging the Gap between Science and Relational DBMS
SciQL, Bridging the Gap between Science and Relational DBMS
PlanetData Network of Excellence
The talk was delivered at the Conference on Data Mining and Data Warehouses (SiKDD 2011) on the 10th of October 2011 in Ljubljana, Slovenia. Publication: http://bit.ly/zrpZ35 Abstract: In this paper we examine how the high dimensionality of oceanographic sensor data impacts the potential use of nearest-neighbor machine learning methods. We focus on one particular consequence of the curse of dimensionality – hubness. We examine the hubness of oceanographic data and show how it can be used to visualize and detect both prototypical sensors/locations, as well as ambiguous and potentially erroneous ones. We proceed to define an easy classification problem on the data, showing that the recently developed hubness-aware classification methods may help to overcome some of the hubness-related issues in sensor data.
Exploring The Hubness-Related Properties of Oceanographic Sensor Data
Exploring The Hubness-Related Properties of Oceanographic Sensor Data
PlanetData Network of Excellence
The paper was delivered in the Proceedings of the 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing (ICCP 2011) on August 26th, 2011 in Cluj-Napoca, Romania. Publication: http://bit.ly/GDIsRY Abstract: OntoGen is a semi-automatic and data-driven ontology editor focusing on editing of topic ontologies. It utilizes text mining tools to make the ontology-related tasks simpler to the user. This focus on building ontologies from textual data is what we are trying to bridge. We have successfully extended OntoGen to work with image data and allow for ontology construction and editing on collections of labeled or unlabeled images. Browsing large heterogeneous image collections efficiently is certainly a challenging task and we feel that semiautomatic ontology construction, as described in this paper, makes this task easier.
OntoGen Extension for Exploring Image Collections
OntoGen Extension for Exploring Image Collections
PlanetData Network of Excellence
The presentation will be delivered by Thanh-Nguyen Ngo at the 14th Asia-Pacific Web Conference (APWeb) on April 12th, 2012 in Kunming, China. Publication: http://bit.ly/yD18Vj Abstract: This paper presents a new approach to measuring similarity over massive time-series data. Our approach is built on two principles: one is to parallelize the large amount computation using a scalable cloud serving system, called TimeCloud. The another is to benet from the lter-and-renement approach for query processing, such that similarity computation is eciently performed over approximated data at the lter step, and then the following renement step measures precise similarities for only a small number of candidates resulted from the ltering. To this end, we establish a set of rm theoretical backgrounds, as well as techniques for processing kNN queries. Our experimental results suggest that the approach proposed is ecient and scalable.
Model based similarity measure in time cloud
Model based similarity measure in time cloud
PlanetData Network of Excellence
The talk was delivered by Surender Reddy Yerva at the International Conference on Web Intelligence, Mining and Semantics (WIMS'2011), May 27, 2011, Sogndal, Norway. Publication: http://bit.ly/ywlLUy Abstract: Twitter is a micro-blogging service on the Web, where people can enter short messages, which then become visible to other users of the service. While the topics of these messages varies, there are a lot of messages where the users express their opinions about com- panies or products. Since the twitter service is very popular, the messages form a rich source of information for companies. They can learn with the help of data mining and sentiment analysis techniques, how their customers like their products or what is the general perception of the company. There is however a great obstacle for analyzing the data directly: as the company names are often ambiguous, one needs first to identify, which messages are related to the company. In this paper we address this question. We present various techniques to classify tweet messages, whether they are related to a given company or not, for example, whether a mes- sage containing the keyword “apple” is about the company Apple Inc.. We present simple techniques, which make use of company profiles, which we created semi-automatically from external Web sources. Our advanced techniques take ambiguity estimations into account and also automatically extend the company profiles from the twitter stream itself. We demonstrate the effectiveness of our methods through an extensive set of experiments.
What have fruits to do with technology? The case of Orange, Blackberry and Apple
What have fruits to do with technology? The case of Orange, Blackberry and Apple
PlanetData Network of Excellence
by Ying Zhang, Martin Kersten, Niels Nes CWI Amsterdam Dec. 03, 2012
SciQL, Bridging the Gap between Science and Relational DBMS
SciQL, Bridging the Gap between Science and Relational DBMS
PlanetData Network of Excellence
A Contextualized Knowledge Repository for Open Data about Trentino
A Contextualized Knowledge Repository for Open Data about Trentino
PlanetData Network of Excellence
Recommended
Secondpresentation
Secondpresentation
mdanielavillagrandesign
Presented at the 15th International Database Engineering & Applications Symposium (IDEAS 2011), September 21-23, 2011, Lisbon, Portugal.
SciQL, Bridging the Gap between Science and Relational DBMS
SciQL, Bridging the Gap between Science and Relational DBMS
PlanetData Network of Excellence
The talk was delivered at the Conference on Data Mining and Data Warehouses (SiKDD 2011) on the 10th of October 2011 in Ljubljana, Slovenia. Publication: http://bit.ly/zrpZ35 Abstract: In this paper we examine how the high dimensionality of oceanographic sensor data impacts the potential use of nearest-neighbor machine learning methods. We focus on one particular consequence of the curse of dimensionality – hubness. We examine the hubness of oceanographic data and show how it can be used to visualize and detect both prototypical sensors/locations, as well as ambiguous and potentially erroneous ones. We proceed to define an easy classification problem on the data, showing that the recently developed hubness-aware classification methods may help to overcome some of the hubness-related issues in sensor data.
Exploring The Hubness-Related Properties of Oceanographic Sensor Data
Exploring The Hubness-Related Properties of Oceanographic Sensor Data
PlanetData Network of Excellence
The paper was delivered in the Proceedings of the 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing (ICCP 2011) on August 26th, 2011 in Cluj-Napoca, Romania. Publication: http://bit.ly/GDIsRY Abstract: OntoGen is a semi-automatic and data-driven ontology editor focusing on editing of topic ontologies. It utilizes text mining tools to make the ontology-related tasks simpler to the user. This focus on building ontologies from textual data is what we are trying to bridge. We have successfully extended OntoGen to work with image data and allow for ontology construction and editing on collections of labeled or unlabeled images. Browsing large heterogeneous image collections efficiently is certainly a challenging task and we feel that semiautomatic ontology construction, as described in this paper, makes this task easier.
OntoGen Extension for Exploring Image Collections
OntoGen Extension for Exploring Image Collections
PlanetData Network of Excellence
The presentation will be delivered by Thanh-Nguyen Ngo at the 14th Asia-Pacific Web Conference (APWeb) on April 12th, 2012 in Kunming, China. Publication: http://bit.ly/yD18Vj Abstract: This paper presents a new approach to measuring similarity over massive time-series data. Our approach is built on two principles: one is to parallelize the large amount computation using a scalable cloud serving system, called TimeCloud. The another is to benet from the lter-and-renement approach for query processing, such that similarity computation is eciently performed over approximated data at the lter step, and then the following renement step measures precise similarities for only a small number of candidates resulted from the ltering. To this end, we establish a set of rm theoretical backgrounds, as well as techniques for processing kNN queries. Our experimental results suggest that the approach proposed is ecient and scalable.
Model based similarity measure in time cloud
Model based similarity measure in time cloud
PlanetData Network of Excellence
The talk was delivered by Surender Reddy Yerva at the International Conference on Web Intelligence, Mining and Semantics (WIMS'2011), May 27, 2011, Sogndal, Norway. Publication: http://bit.ly/ywlLUy Abstract: Twitter is a micro-blogging service on the Web, where people can enter short messages, which then become visible to other users of the service. While the topics of these messages varies, there are a lot of messages where the users express their opinions about com- panies or products. Since the twitter service is very popular, the messages form a rich source of information for companies. They can learn with the help of data mining and sentiment analysis techniques, how their customers like their products or what is the general perception of the company. There is however a great obstacle for analyzing the data directly: as the company names are often ambiguous, one needs first to identify, which messages are related to the company. In this paper we address this question. We present various techniques to classify tweet messages, whether they are related to a given company or not, for example, whether a mes- sage containing the keyword “apple” is about the company Apple Inc.. We present simple techniques, which make use of company profiles, which we created semi-automatically from external Web sources. Our advanced techniques take ambiguity estimations into account and also automatically extend the company profiles from the twitter stream itself. We demonstrate the effectiveness of our methods through an extensive set of experiments.
What have fruits to do with technology? The case of Orange, Blackberry and Apple
What have fruits to do with technology? The case of Orange, Blackberry and Apple
PlanetData Network of Excellence
by Ying Zhang, Martin Kersten, Niels Nes CWI Amsterdam Dec. 03, 2012
SciQL, Bridging the Gap between Science and Relational DBMS
SciQL, Bridging the Gap between Science and Relational DBMS
PlanetData Network of Excellence
A Contextualized Knowledge Repository for Open Data about Trentino
A Contextualized Knowledge Repository for Open Data about Trentino
PlanetData Network of Excellence
The talk was delivered by Ying Zhang at the the First International Array Databases Workshop , co-located with the EDBT/ICDT 2011 Joint Conference on March 25, 2011 in Uppsala, Sweden. Publication: http://bit.ly/zyQPBq Abstract: Scientific applications are still poorly served by contemporary relational database systems. At best, the system provides a bridge towards an external library using user-defined functions, explicit import/export facilities or linked-in Java/C# interpreters. Time has come to rectify this with SciQL1, a SQL query language for scientific applications with arrays as first class citizens. It provides a seamless symbiosis of array-, set-, and sequence- interpretation using a clear separation of the mathematical object from its underlying implementation. A key innovation is to extend valuebased grouping in SQL:2003 with structural grouping, i.e., fixedsized and unbounded groups based on explicit relationships between their dimension attributes. It leads to a generalization of window-based query processing with wide applicability in science domains. This paper is focused on the language features, extensively illustrated with examples of its intended use.
SciQL, A Query Language for Science Applications
SciQL, A Query Language for Science Applications
PlanetData Network of Excellence
by Giorgos Flouris at Open Data Tutorials, May 2013
Data and Knowledge Evolution
Data and Knowledge Evolution
PlanetData Network of Excellence
by Oscar Corcho @ISWC2013 Workshop on Ordering and Reasoning, Sydney, 22/10/2013
On the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream Processing
PlanetData Network of Excellence
by Irene Celino, Dario Cerizza, Simone Contessa, Marta Corubolo, Daniele Dell’Aglio, Emanuele Della Valle, Stefano Fumeo and Federico Piccinini Semantic Web Challenge @ ISWC 2012 - 2012/11/14
Urbanopoly: Collection and Quality Assessment of Geo-spatial Linked Data via ...
Urbanopoly: Collection and Quality Assessment of Geo-spatial Linked Data via ...
PlanetData Network of Excellence
The presentation was delivered during the 1st International Conference on Health Information Science (HIS 2012) on April 9th, 2012 in Beijing, China. Abstract: In cytomics bookkeeping of the data generated during lab experiments is crucial. The current approach in cytomics is to conduct High-Throughput Screening (HTS) experiments so that cells can be tested under many different experimental conditions. Given the large amount of different conditions and the readout of the conditions through images, it is clear that the HTS approach requires a proper data management system to reduce the time needed for experiments and the chance of man-made errors. As different types of data exist, the experimental conditions need to be linked to the images produced by the HTS experiments with their metadata and the results of further analysis. Moreover, HTS experiments never stand by themselves, as more experiments are lined up, the amount of data and computations needed to analyze these increases rapidly. To that end cytomic experiments call for automated and systematic solutions that provide convenient and robust features for scientists to manage and analyze their data. In this paper, we propose a platform for managing and analyzing HTS images resulting from cytomics screens taking the automated HTS workflow as a starting point. This platform seamlessly integrates the whole HTS workflow into a single system. The platform relies on a modern relational database system to store user data and process user requests, while providing a convenient web interface to end-users. By implementing this platform, the overall workload of HTS experiments, from experiment design to data analysis, is reduced significantly. Additionally, the platform provides the potential for data integration to accomplish genotype-to-phenotype modeling studies.
Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and ...
Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and ...
PlanetData Network of Excellence
The talk was given at the 15th International Conference on Extending Database Technology (EDBT 2012) on March 29, 2012 in Berlin, Germany. Abstract: Query optimization in RDF Stores is a challenging problem as SPARQL queries typically contain many more joins than equivalent relational plans, and hence lead to a large join order search space. In such cases, cost-based query optimization often is not possible. One practical reason for this is that statistics typically are missing in web scale setting such as the Linked Open Datasets (LOD). The more profound reason is that due to the absence of schematic structure in RDF, join-hit ratio estimation requires complicated forms of correlated join statistics; and currently there are no methods to identify the relevant correlations beforehand. For this reason, the use of good heuristics is essential in SPARQL query optimization, even in the case that are partially used with cost-based statistics (i.e., hybrid query optimization). In this paper we describe a set of useful heuristics for SPARQL query optimizers. We present these in the context of a new Heuristic SPARQL Planner (HSP) that is capable of exploiting the syntactic and the structural variations of the triple patterns in a SPARQL query in order to choose an execution plan without the need of any cost model. For this, we define the variable graph and we show a reduction of the SPARQL query optimization problem to the maximum weight independent set problem. We implemented our planner on top of the MonetDB open source column-store and evaluated its effectiveness against the state-of-the-art RDF-3X engine as well as comparing the plan quality with a relational (SQL) equivalent of the benchmarks.
Heuristic based Query Optimisation for SPARQL
Heuristic based Query Optimisation for SPARQL
PlanetData Network of Excellence
The presentation was delivered by FORTH at the 3rd International Workshop on the role of Semantic Web in Provenance Management 2012 (SWPM2012) in Heraklion, Greece on 28th of May 2012. Abstract: Workflow systems can produce very large amounts of provenance information. In this paper we introduce provenance-based inference rules as a means to reduce the amount of provenance information that has to be stored, and to ease quality control (e.g., corrections). We motivate this kind of (provenance) inference and identify a number of basic inference rules over a conceptual model appropriate for representing provenance. The proposed inference rules concern the interplay between (i) actors and carried out activities, (ii) activities and devices that were used for such activities, and, (iii) the presence of information objects and physical things at events. However, since a knowledge base is not static but it changes over time for various reasons, we also study how we can satisfy change requests while supporting and respecting the aforementioned inference rules. Towards this end, we elaborate on the specification of the required change operations.
Evolution of Workflow Provenance Information in the Presence of Custom Infere...
Evolution of Workflow Provenance Information in the Presence of Custom Infere...
PlanetData Network of Excellence
In this presentation we review some of the research problems we address at EPFL in the area of sensor data management. At the level of infrastructure we have developed a middleware to seamlessly integrate, aggregate and analyze heterogeneous sensor data streams in real-time, a WIKI based repository supporting the cooperative management of the metadata associated with sensor deployments and cloud-based storage infrastructure. An important problem in managing sensor data is their efficient storage and transmission using compression techniques. To that end we apply model-based compression methods. For analyzing sensor data, we have developed methods to dynamically estimate the variability, which can be readily used for outlier detection, and to extract semantic features from GPS sensor data streams. We also investigate techniques for trading off between the accuracy of the sensor data obtained and the degree of privacy preservation that can be maintained. The Sensor Data Management presentation was presented by Karl Aberer (Ecole Polytechnique Federale de Lausanne) at the PlanetData project Meeting on February 28 - March 4, 2011 in Innsbruck, Austria.
Sensor Data Management
Sensor Data Management
PlanetData Network of Excellence
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The talk was delivered by Ying Zhang at the the First International Array Databases Workshop , co-located with the EDBT/ICDT 2011 Joint Conference on March 25, 2011 in Uppsala, Sweden. Publication: http://bit.ly/zyQPBq Abstract: Scientific applications are still poorly served by contemporary relational database systems. At best, the system provides a bridge towards an external library using user-defined functions, explicit import/export facilities or linked-in Java/C# interpreters. Time has come to rectify this with SciQL1, a SQL query language for scientific applications with arrays as first class citizens. It provides a seamless symbiosis of array-, set-, and sequence- interpretation using a clear separation of the mathematical object from its underlying implementation. A key innovation is to extend valuebased grouping in SQL:2003 with structural grouping, i.e., fixedsized and unbounded groups based on explicit relationships between their dimension attributes. It leads to a generalization of window-based query processing with wide applicability in science domains. This paper is focused on the language features, extensively illustrated with examples of its intended use.
SciQL, A Query Language for Science Applications
SciQL, A Query Language for Science Applications
PlanetData Network of Excellence
by Giorgos Flouris at Open Data Tutorials, May 2013
Data and Knowledge Evolution
Data and Knowledge Evolution
PlanetData Network of Excellence
by Oscar Corcho @ISWC2013 Workshop on Ordering and Reasoning, Sydney, 22/10/2013
On the need for a W3C community group on RDF Stream Processing
On the need for a W3C community group on RDF Stream Processing
PlanetData Network of Excellence
by Irene Celino, Dario Cerizza, Simone Contessa, Marta Corubolo, Daniele Dell’Aglio, Emanuele Della Valle, Stefano Fumeo and Federico Piccinini Semantic Web Challenge @ ISWC 2012 - 2012/11/14
Urbanopoly: Collection and Quality Assessment of Geo-spatial Linked Data via ...
Urbanopoly: Collection and Quality Assessment of Geo-spatial Linked Data via ...
PlanetData Network of Excellence
The presentation was delivered during the 1st International Conference on Health Information Science (HIS 2012) on April 9th, 2012 in Beijing, China. Abstract: In cytomics bookkeeping of the data generated during lab experiments is crucial. The current approach in cytomics is to conduct High-Throughput Screening (HTS) experiments so that cells can be tested under many different experimental conditions. Given the large amount of different conditions and the readout of the conditions through images, it is clear that the HTS approach requires a proper data management system to reduce the time needed for experiments and the chance of man-made errors. As different types of data exist, the experimental conditions need to be linked to the images produced by the HTS experiments with their metadata and the results of further analysis. Moreover, HTS experiments never stand by themselves, as more experiments are lined up, the amount of data and computations needed to analyze these increases rapidly. To that end cytomic experiments call for automated and systematic solutions that provide convenient and robust features for scientists to manage and analyze their data. In this paper, we propose a platform for managing and analyzing HTS images resulting from cytomics screens taking the automated HTS workflow as a starting point. This platform seamlessly integrates the whole HTS workflow into a single system. The platform relies on a modern relational database system to store user data and process user requests, while providing a convenient web interface to end-users. By implementing this platform, the overall workload of HTS experiments, from experiment design to data analysis, is reduced significantly. Additionally, the platform provides the potential for data integration to accomplish genotype-to-phenotype modeling studies.
Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and ...
Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and ...
PlanetData Network of Excellence
The talk was given at the 15th International Conference on Extending Database Technology (EDBT 2012) on March 29, 2012 in Berlin, Germany. Abstract: Query optimization in RDF Stores is a challenging problem as SPARQL queries typically contain many more joins than equivalent relational plans, and hence lead to a large join order search space. In such cases, cost-based query optimization often is not possible. One practical reason for this is that statistics typically are missing in web scale setting such as the Linked Open Datasets (LOD). The more profound reason is that due to the absence of schematic structure in RDF, join-hit ratio estimation requires complicated forms of correlated join statistics; and currently there are no methods to identify the relevant correlations beforehand. For this reason, the use of good heuristics is essential in SPARQL query optimization, even in the case that are partially used with cost-based statistics (i.e., hybrid query optimization). In this paper we describe a set of useful heuristics for SPARQL query optimizers. We present these in the context of a new Heuristic SPARQL Planner (HSP) that is capable of exploiting the syntactic and the structural variations of the triple patterns in a SPARQL query in order to choose an execution plan without the need of any cost model. For this, we define the variable graph and we show a reduction of the SPARQL query optimization problem to the maximum weight independent set problem. We implemented our planner on top of the MonetDB open source column-store and evaluated its effectiveness against the state-of-the-art RDF-3X engine as well as comparing the plan quality with a relational (SQL) equivalent of the benchmarks.
Heuristic based Query Optimisation for SPARQL
Heuristic based Query Optimisation for SPARQL
PlanetData Network of Excellence
The presentation was delivered by FORTH at the 3rd International Workshop on the role of Semantic Web in Provenance Management 2012 (SWPM2012) in Heraklion, Greece on 28th of May 2012. Abstract: Workflow systems can produce very large amounts of provenance information. In this paper we introduce provenance-based inference rules as a means to reduce the amount of provenance information that has to be stored, and to ease quality control (e.g., corrections). We motivate this kind of (provenance) inference and identify a number of basic inference rules over a conceptual model appropriate for representing provenance. The proposed inference rules concern the interplay between (i) actors and carried out activities, (ii) activities and devices that were used for such activities, and, (iii) the presence of information objects and physical things at events. However, since a knowledge base is not static but it changes over time for various reasons, we also study how we can satisfy change requests while supporting and respecting the aforementioned inference rules. Towards this end, we elaborate on the specification of the required change operations.
Evolution of Workflow Provenance Information in the Presence of Custom Infere...
Evolution of Workflow Provenance Information in the Presence of Custom Infere...
PlanetData Network of Excellence
In this presentation we review some of the research problems we address at EPFL in the area of sensor data management. At the level of infrastructure we have developed a middleware to seamlessly integrate, aggregate and analyze heterogeneous sensor data streams in real-time, a WIKI based repository supporting the cooperative management of the metadata associated with sensor deployments and cloud-based storage infrastructure. An important problem in managing sensor data is their efficient storage and transmission using compression techniques. To that end we apply model-based compression methods. For analyzing sensor data, we have developed methods to dynamically estimate the variability, which can be readily used for outlier detection, and to extract semantic features from GPS sensor data streams. We also investigate techniques for trading off between the accuracy of the sensor data obtained and the degree of privacy preservation that can be maintained. The Sensor Data Management presentation was presented by Karl Aberer (Ecole Polytechnique Federale de Lausanne) at the PlanetData project Meeting on February 28 - March 4, 2011 in Innsbruck, Austria.
Sensor Data Management
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SciQL, A Query Language for Science Applications
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Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and ...
Automation in Cytomics: A Modern RDBMS Based Platform for Image Analysis and ...
Heuristic based Query Optimisation for SPARQL
Heuristic based Query Optimisation for SPARQL
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Second Presentation
1.
WEARABLE TECHNOLOGY FOR
IMPROVING SPECIAL NEEDS JEWELLERY+TECHNOLOGY
2.
EPILEPSY “One in
every 131 people in the UK has epilepsy ”. (The National Society for Epilepsy 2010).
3.
WHO ARE MY
EXPERTS? Parents and legal guardians of a person with epilepsy
4.
WHAT CHANGED? Identifying
the most important and problematic areas in epilepsy management
5.
?
6.
WHAT’S NEXT?
7.
Daniela Villagrán m.villagranrivas@dundee.ac.uk
THANK YOU…
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