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366 results on '"Manegold, S. (Stefan)"'

1. Message from the Chairs

Author

Chen, L. (Lei), Manegold, S. (Stefan), Mehrotra, S. (Sharad), Li, C. (Chen), Chen, L. (Lei), Manegold, S. (Stefan), Mehrotra, S. (Sharad), and Li, C. (Chen)

Published
2023
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2. Multidimensional adaptive & progressive indexes

Author

Nerone, M. (Matheus), Holanda, P.T. (Pedro), Almeida, E.C. (Eduardo) de, Manegold, S. (Stefan), Nerone, M. (Matheus), Holanda, P.T. (Pedro), Almeida, E.C. (Eduardo) de, and Manegold, S. (Stefan)

Abstract

Exploratory data analysis is the primary technique used by data scientists to extract knowledge from new data sets. This type of workload is composed of trial-and-error hypothesis-driven queries with a human in the loop. To keep up with the data scientist's productivity, the system must be capable of answering queries in interactive times. Given that these queries are highly selective multidimensional queries, multidimensional indexes are necessary to ensure low latency. However, creating the appropriate indexes is not a given due to the highly exploratory and interactive nature of such human-in-the-loop scenarios.In this paper, we identify four main objectives that are desirable for exploratory data analysis workloads: (1) low overhead over the initial queries, (2) low query variance (i.e., high robustness), (3) predictable index convergence, and (4) low total workload time. Given that not all of them can be achieved at the same time, we present three novel incremental multidimensional indexing techniques that represent three sample points on a Pareto front for this multi-objective optimization problem. (a) The Adaptive KD-Tree is designed to achieve the lowest total workload time at the expense of a higher indexing penalty for the initial queries, lack of robustness, and unpredictable convergence. (b) The Progressive KD-Tree has predictable convergence and a user-defined indexing cost for the initial queries. However, total workload time can be higher than with Adaptive KD-Trees, and per-query time still varies. (c) The Greedy Progressive KD-Tree aims at full robustness at the expense of only improving the per-query cost after full index convergence.Our extensive experimental evaluation using both synthetic and real-life data sets and workloads shows that (a) the Adaptive KD-Tree reduce

Published
2021
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3. Progressive Mergesort: Merging batches of appends into progressive indexes

Author

Holanda, P.T. (Pedro), Manegold, S. (Stefan), Holanda, P.T. (Pedro), and Manegold, S. (Stefan)

Abstract

Interactive exploratory data analysis consists of workloads that are composed of filter-aggregate queries with highly selective filters [1]. Hence, their performance is dependent on how much data they can skip during their scans, with indexes being the most efficient technique for aggressive data-skipping. Progressive Indexes are the state-of-the-art on automatic index creation for interactive exploratory data analysis. These indexes are partially constructed during query execution, eventually refining to a full index. However, progressive indexes have been designed for static databases, while in exploratory data analysis updates - usually batch-appends of newly acquired data - are frequent. In this paper, we propose Progressive Mergesort, a novel merging technique to make Progressive Indexes cope with updates. Progressive Mergesort differs from other merging techniques for partial indexes as it incorporates the index budget strategy design from Progressive Indexing. It follows the same three principles as Progressive Indexes: (1) fast query execution, (2) high robustness,(3) guaranteed convergence. Our experimental evaluation demonstrates that Progressive Mergesort is capable of achieving a 2x speedup when merging updates and up to 3 orders of magnitude lower variance than the state of the art.

Published
2021
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4. Progressive join algorithms considering user preference

Author

Ding, M. (Mengsu), Chen, S. (Shimin), Makrynioti, K. (Nantia), Manegold, S. (Stefan), Ding, M. (Mengsu), Chen, S. (Shimin), Makrynioti, K. (Nantia), and Manegold, S. (Stefan)

Abstract

Progressive query processing is a new attractive paradigm for exploratory data analysis. This paper considers the case where users want to receive results ordered according to their preference, and specifically focuses on the design of join algorithms. We investigate the use of contour lines in progressive algorithms with user preferences, and propose ContourJoin to reduce sorting overhead of progressive preference-aware joins. Experimental results show that compared with the na ̈ıve blocking algorithm and the top-k RankJoin algorithm, ContourJoin has superior performance in both early result generation and total result computation.

Published
2021

5. Progressive Mergesort: Merging batches of appends into progressive indexes

Author

Timbó Holanda, P.T. (Pedro), Manegold, S. (Stefan), Timbó Holanda, P.T. (Pedro), and Manegold, S. (Stefan)

Abstract

Interactive exploratory data analysis consists of workloads that are composed of filter-aggregate queries with highly selective filters [1]. Hence, their performance is dependent on how much data they can skip during their scans, with indexes being the most efficient technique for aggressive data-skipping. Progressive Indexes are the state-of-the-art on automatic index creation for interactive exploratory data analysis. These indexes are partially constructed during query execution, eventually refining to a full index. However, progressive indexes have been designed for static databases, while in exploratory data analysis updates - usually batch-appends of newly acquired data - are frequent. In this paper, we propose Progressive Mergesort, a novel merging technique to make Progressive Indexes cope with updates. Progressive Mergesort differs from other merging techniques for partial indexes as it incorporates the index budget strategy design from Progressive Indexing. It follows the same three principles as Progressive Indexes: (1) fast query execution, (2) high robustness,(3) guaranteed convergence. Our experimental evaluation demonstrates that Progressive Mergesort is capable of achieving a 2x speedup when merging updates and up to 3 orders of magnitude lower variance than the state of the art.

Published
2021
Full Text
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6. Multidimensional adaptive & progressive indexes

Author

Nerone, M. (Matheus), Timbó Holanda, P.T. (Pedro), Almeida, E.C. (Eduardo) de, Manegold, S. (Stefan), Nerone, M. (Matheus), Timbó Holanda, P.T. (Pedro), Almeida, E.C. (Eduardo) de, and Manegold, S. (Stefan)

Abstract

Exploratory data analysis is the primary technique used by data scientists to extract knowledge from new data sets. This type of workload is composed of trial-and-error hypothesis-driven queries with a human in the loop. To keep up with the data scientist's productivity, the system must be capable of answering queries in interactive times. Given that these queries are highly selective multidimensional queries, multidimensional indexes are necessary to ensure low latency. However, creating the appropriate indexes is not a given due to the highly exploratory and interactive nature of such human-in-the-loop scenarios.In this paper, we identify four main objectives that are desirable for exploratory data analysis workloads: (1) low overhead over the initial queries, (2) low query variance (i.e., high robustness), (3) predictable index convergence, and (4) low total workload time. Given that not all of them can be achieved at the same time, we present three novel incremental multidimensional indexing techniques that represent three sample points on a Pareto front for this multi-objective optimization problem. (a) The Adaptive KD-Tree is designed to achieve the lowest total workload time at the expense of a higher indexing penalty for the initial queries, lack of robustness, and unpredictable convergence. (b) The Progressive KD-Tree has predictable convergence and a user-defined indexing cost for the initial queries. However, total workload time can be higher than with Adaptive KD-Trees, and per-query time still varies. (c) The Greedy Progressive KD-Tree aims at full robustness at the expense of only improving the per-query cost after full index convergence.Our extensive experimental evaluation using both synthetic and real-life data sets and workloads shows that (a) the Adaptive KD-Tree reduces total workload time by up to a factor 2 compared to the state-of-the-art, (b) the Progressive KD-Tree achieves predictable convergence with up to one order of magnitude lower init

Published
2021
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8. DevUDf: Increasing UDF development efficiency through IDE integration. It works like a PyCharm!

Author

Raasveldt, M. (Mark), Holanda, P.T. (Pedro), Manegold, S. (Stefan), Raasveldt, M. (Mark), Holanda, P.T. (Pedro), and Manegold, S. (Stefan)

Abstract

User-defined functions (UDFs) facilitate the execution of analytics pipelines inside the database. They provide many advantages over traditional methods, such as close-to-data execution and automatic parallelization. However, the standard workflow for developing and debugging UDFs does not allow developers to use their regular toolchains and Integrated Development Environments (IDEs). As a result, writing functional UDFs is challenging. In this demo, we present the devUDF, a plugin to the PyCharm IDE that allows developers to develop and debug their MonetDB/Python UDFs directly from within the IDE.

Published
2019
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9. SQALPEL: A database performance platform

Author

Kersten, M.L. (Martin), Koutsourakis, P. (Panagiotis), Manegold, S. (Stefan), Zhang, Y. (Ying), Kersten, M.L. (Martin), Koutsourakis, P. (Panagiotis), Manegold, S. (Stefan), and Zhang, Y. (Ying)

Abstract

Despite their popularity, database benchmarks only highlight a small fraction of the capabilities of any given DBMS. They often do not highlight problematic components encountered in real life database applications or provide hints for further research and engineering. To alleviate this problem we coined discriminative performance benchmarking as the way to go. It aids in exploring a larger query search space to find performance outliers and their underlying cause. The approach is based on deriving a domain specific language from a sample complex query to identify and execute a query workload. The demo illustrates sqalpel, a complete platform to collect, manage and selectively disseminate performance facts, that enables repeatability studies, and economy of scale by sharing performance experiences.

Published
2019

11. Progressive Indexes: Indexing for interactive data analysis

Author

Holanda, P.T. (Pedro), Raasveldt, M. (Mark), Manegold, S. (Stefan), Mühleisen, H.F. (Hannes), Holanda, P.T. (Pedro), Raasveldt, M. (Mark), Manegold, S. (Stefan), and Mühleisen, H.F. (Hannes)

Abstract

Interactive exploration of large volumes of data is increasingly common, as data scientists attempt to extract interesting information from large opaque data sets. This scenario presents a difficult challenge for traditional database systems, as (1) nothing is known about the query workload in advance, (2) the query workload is constantly changing, and (3) the system must provide interactive responses to the issued queries. This environment is challenging for index creation, as traditional database indexes require upfront creation, hence a priori workload knowledge, to be efficient.In this paper, we introduce Progressive Indexing, a novel performance-driven indexing technique that focuses on automatic index creation while providing interactive response times to incoming queries. Its design allows queries to have a limited budget to spend on index creation. The indexing budget is automatically tuned to each query before query processing. This allows for systems to provide interactive answers to queries during index creation while being robust against various workload patterns and data distributions.

Published
2019
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12. Public BI benchmark

Author

Ghita, B. (Bogdan), Manegold, S. (Stefan), Boncz, P.A. (Peter), Ghita, B. (Bogdan), Manegold, S. (Stefan), and Boncz, P.A. (Peter)

Abstract

User generated benchmark derived from the DBTest'18 paper by Tableau. It contains real data and queries from 46 public workbooks in Tableau Public.

Published
2019

13. Progressive Indexes: Indexing for interactive data analysis

Author

Timbó Holanda, P.T. (Pedro), Raasveldt, M. (Mark), Manegold, S. (Stefan), Mühleisen, H.F. (Hannes), Timbó Holanda, P.T. (Pedro), Raasveldt, M. (Mark), Manegold, S. (Stefan), and Mühleisen, H.F. (Hannes)

Abstract

Interactive exploration of large volumes of data is increasingly common, as data scientists attempt to extract interesting information from large opaque data sets. This scenario presents a difficult challenge for traditional database systems, as (1) nothing is known about the query workload in advance, (2) the query workload is constantly changing, and (3) the system must provide interactive responses to the issued queries. This environment is challenging for index creation, as traditional database indexes require upfront creation, hence a priori workload knowledge, to be efficient.In this paper, we introduce Progressive Indexing, a novel performance-driven indexing technique that focuses on automatic index creation while providing interactive response times to incoming queries. Its design allows queries to have a limited budget to spend on index creation. The indexing budget is automatically tuned to each query before query processing. This allows for systems to provide interactive answers to queries during index creation while being robust against various workload patterns and data distributions.

Published
2019
Full Text
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16. SQALPEL: A database performance platform

Author

Kersten, M.L. (Martin), Koutsourakis, P. (Panagiotis), Manegold, S. (Stefan), Zhang, Y. (Ying), Kersten, M.L. (Martin), Koutsourakis, P. (Panagiotis), Manegold, S. (Stefan), and Zhang, Y. (Ying)

Abstract

Despite their popularity, database benchmarks only highlight a small fraction of the capabilities of any given DBMS. They often do not highlight problematic components encountered in real life database applications or provide hints for further research and engineering. To alleviate this problem we coined discriminative performance benchmarking as the way to go. It aids in exploring a larger query search space to find performance outliers and their underlying cause. The approach is based on deriving a domain specific language from a sample complex query to identify and execute a query workload. The demo illustrates sqalpel, a complete platform to collect, manage and selectively disseminate performance facts, that enables repeatability studies, and economy of scale by sharing performance experiences.

Published
2019

17. DevUDf: Increasing UDF development efficiency through IDE integration. It works like a PyCharm!

Author

Raasveldt, M. (Mark), Timbó Holanda, P.T. (Pedro), Manegold, S. (Stefan), Raasveldt, M. (Mark), Timbó Holanda, P.T. (Pedro), and Manegold, S. (Stefan)

Abstract

User-defined functions (UDFs) facilitate the execution of analytics pipelines inside the database. They provide many advantages over traditional methods, such as close-to-data execution and automatic parallelization. However, the standard workflow for developing and debugging UDFs does not allow developers to use their regular toolchains and Integrated Development Environments (IDEs). As a result, writing functional UDFs is challenging. In this demo, we present the devUDF, a plugin to the PyCharm IDE that allows developers to develop and debug their MonetDB/Python UDFs directly from within the IDE.

Published
2019
Full Text
View/download PDF

18. GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

Author

Kyzirakos, K. (Konstantinos), Savva, D. (Dimitrianos), Vlachopoulos, I. (Ioannis), Vasileiou, A. (Alexandros), Karalis, N. (Nikolaos), Koubarakis, M. (Manolis), Manegold, S. (Stefan), Kyzirakos, K. (Konstantinos), Savva, D. (Dimitrianos), Vlachopoulos, I. (Ioannis), Vasileiou, A. (Alexandros), Karalis, N. (Nikolaos), Koubarakis, M. (Manolis), and Manegold, S. (Stefan)

Abstract

A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop.

Published
2018
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19. From big data to big information and big knowledge: The case of Earth observation data

Author

Bereta, K. (Konstantina), Koubarakis, M. (Manolis), Manegold, S. (Stefan), Stamoulis, G. (Georgios), Demir, B. (Begüm), Bereta, K. (Konstantina), Koubarakis, M. (Manolis), Manegold, S. (Stefan), Stamoulis, G. (Georgios), and Demir, B. (Begüm)

Abstract

Some particularly important rich sources of open and free big geospatial data are the Earth observation (EO) programs of various countries such as the Landsat program of the US and the Copernicus programme of the European Union. EO data is a paradigmatic case of big data and the same is true for the big information and big knowledge extracted from it. EO data (satellite images and in-situ data), and the information and knowledge extracted from it, can be utilized in many applications with financial and environmental impact in areas such as emergency management, climate change, agriculture and security.

Published
2018
Full Text
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20. Cracking KD-Tree: The first multidimensional adaptive indexing

Author

Holanda, P.T. (Pedro), Nerone, M. (Matheus), Almeida, E.C. (Eduardo) de, Manegold, S. (Stefan), Holanda, P.T. (Pedro), Nerone, M. (Matheus), Almeida, E.C. (Eduardo) de, and Manegold, S. (Stefan)

Abstract

Workload-aware physical data access structures are crucial to achieve short response time with (exploratory) data analysis tasks as commonly required for Big Data and Data Science applications. Recently proposed techniques such as automatic index advisers (for a priori known static workloads) and query-driven adaptive incremental indexing (for a priori unknown dynamic workloads) form the state-of-the-art to build single-dimensional indexes for single-attribute query predicates. However, similar techniques for more demanding multi-attribute query predicates, which are vital for any data analysis task, have not been proposed, yet. In this paper, we present our on-going work on a new set of workload-adaptive indexing techniques that focus on creating multidimensional indexes. We present our proof-of-concept, the Cracking KD-Tree, an adaptive indexing approach that generates a KD-Tree based on multidimensional range query predicates. It works by incrementally creating partial multidimensional indexes as a by-product of query processing. The indexes are produced only on those parts of the data that are accessed, and their creation cost is effectively distributed across a stream of queries. Experimental results show that the Cracking KD-Tree is three times faster than creating a full KD-Tree, one order of magnitude faster than executing full scans and two orders of magnitude faster than using uni-dimensional full or adaptive indexes on multiple columns.

Published
2018
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21. Deep integration of machine learning Into column stores

Author

Raasveldt, M. (Mark), Holanda, P.T. (Pedro), Mühleisen, H.F. (Hannes), Manegold, S. (Stefan), Raasveldt, M. (Mark), Holanda, P.T. (Pedro), Mühleisen, H.F. (Hannes), and Manegold, S. (Stefan)

Abstract

We leverage vectorized User-Defined Functions (UDFs) to efficiently integrate unchanged machine learning pipelines into an analytical data management system. The entire pipelines including data, models, parameters and evaluation outcomes are stored and executed inside the database system. Experiments using our MonetDB/Python UDFs show greatly improved performance due to reduced data movement and parallel processing opportunities. In addition, this integration enables meta-analysis of models using relational queries.

Published
2018
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22. Deep integration of machine learning Into column stores

Author

Raasveldt, M. (Mark), Timbó Holanda, P.T. (Pedro), Mühleisen, H.F. (Hannes), Manegold, S. (Stefan), Raasveldt, M. (Mark), Timbó Holanda, P.T. (Pedro), Mühleisen, H.F. (Hannes), and Manegold, S. (Stefan)

Abstract

We leverage vectorized User-Defined Functions (UDFs) to efficiently integrate unchanged machine learning pipelines into an analytical data management system. The entire pipelines including data, models, parameters and evaluation outcomes are stored and executed inside the database system. Experiments using our MonetDB/Python UDFs show greatly improved performance due to reduced data movement and parallel processing opportunities. In addition, this integration enables meta-analysis of models using relational queries.

Published
2018
Full Text
View/download PDF

23. GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

Author

Kyzirakos, K. (Konstantinos), Savva, D. (Dimitrianos), Vlachopoulos, I. (Ioannis), Vasileiou, A. (Alexandros), Karalis, N. (Nikolaos), Koubarakis, M. (Manolis), Manegold, S. (Stefan), Kyzirakos, K. (Konstantinos), Savva, D. (Dimitrianos), Vlachopoulos, I. (Ioannis), Vasileiou, A. (Alexandros), Karalis, N. (Nikolaos), Koubarakis, M. (Manolis), and Manegold, S. (Stefan)

Abstract

A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop.

Published
2018
Full Text
View/download PDF

24. From big data to big information and big knowledge: The case of Earth observation data

Author

Bereta, K. (Konstantina), Koubarakis, M. (Manolis), Manegold, S. (Stefan), Stamoulis, G. (Georgios), Demir, B. (Begüm), Bereta, K. (Konstantina), Koubarakis, M. (Manolis), Manegold, S. (Stefan), Stamoulis, G. (Georgios), and Demir, B. (Begüm)

Abstract

Some particularly important rich sources of open and free big geospatial data are the Earth observation (EO) programs of various countries such as the Landsat program of the US and the Copernicus programme of the European Union. EO data is a paradigmatic case of big data and the same is true for the big information and big knowledge extracted from it. EO data (satellite images and in-situ data), and the information and knowledge extracted from it, can be utilized in many applications with financial and environmental impact in areas such as emergency management, climate change, agriculture and security.

Published
2018
Full Text
View/download PDF

25. Cracking KD-Tree: The first multidimensional adaptive indexing

Author

Timbó Holanda, P.T. (Pedro), Nerone, M. (Matheus), Almeida, E.C. (Eduardo) de, Manegold, S. (Stefan), Timbó Holanda, P.T. (Pedro), Nerone, M. (Matheus), Almeida, E.C. (Eduardo) de, and Manegold, S. (Stefan)

Abstract

Workload-aware physical data access structures are crucial to achieve short response time with (exploratory) data analysis tasks as commonly required for Big Data and Data Science applications. Recently proposed techniques such as automatic index advisers (for a priori known static workloads) and query-driven adaptive incremental indexing (for a priori unknown dynamic workloads) form the state-of-the-art to build single-dimensional indexes for single-attribute query predicates. However, similar techniques for more demanding multi-attribute query predicates, which are vital for any data analysis task, have not been proposed, yet. In this paper, we present our on-going work on a new set of workload-adaptive indexing techniques that focus on creating multidimensional indexes. We present our proof-of-concept, the Cracking KD-Tree, an adaptive indexing approach that generates a KD-Tree based on multidimensional range query predicates. It works by incrementally creating partial multidimensional indexes as a by-product of query processing. The indexes are produced only on those parts of the data that are accessed, and their creation cost is effectively distributed across a stream of queries. Experimental results show that the Cracking KD-Tree is three times faster than creating a full KD-Tree, one order of magnitude faster than executing full scans and two orders of magnitude faster than using uni-dimensional full or adaptive indexes on multiple columns.

Published
2018
Full Text
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26. Managing big, linked, and open earth-observation data: Using the TELEIOS/LEO software stack

Author

Koubarakis, M. (Manolis), Kyzirakos, K. (Konstantinos), Nikolaou, C. (Charalampos), Garbis, G. (George), Bereta, K. (Konstantina), Dogani, R. (Roi), Giannakopoulou, S. (Stella), Smeros, P. (Panayiotis), Savva, D. (Dimitrianos), Stamoulis, G. (Georgios), Vlachopoulos, G. (Giannis), Manegold, S. (Stefan), Kontoes, C. (Charalampos), Herekakis, T. (Themistocles), Papoutsis, I. (Ioannis), Michail, D. (Dimitrios), Koubarakis, M. (Manolis), Kyzirakos, K. (Konstantinos), Nikolaou, C. (Charalampos), Garbis, G. (George), Bereta, K. (Konstantina), Dogani, R. (Roi), Giannakopoulou, S. (Stella), Smeros, P. (Panayiotis), Savva, D. (Dimitrianos), Stamoulis, G. (Georgios), Vlachopoulos, G. (Giannis), Manegold, S. (Stefan), Kontoes, C. (Charalampos), Herekakis, T. (Themistocles), Papoutsis, I. (Ioannis), and Michail, D. (Dimitrios)

Abstract

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service.

Published
2016
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27. MonetDB Jul2015-SP2 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), and Koutsourakis, P. (Panagiotis)

Published
2016

28. MonetDB Jun2016 feature release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, and Stalinov, S.G. (Svetlin)

Published
2016

29. MonetDB Jun2016-SP2 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), Leo, D. (Dean) De, Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), and Leo, D. (Dean) De

Published
2016

30. MonetDB Jul2015-SP3 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), and Wits, A. (Abe)

Published
2016

31. MonetDB Jun2016-SP1 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), Leo, D. (Dean) De, Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), and Leo, D. (Dean) De

Published
2016

32. MonetDB Jul2015-SP4 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Döhmen, T.R. (Till), Kruit, B.B. (Benno), and Wits, A. (Abe)

Published
2016

33. MonetDB Jun2016-SP2 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), Leo, D. (Dean) De, Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), and Leo, D. (Dean) De

Published
2016

34. MonetDB Jun2016-SP1 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), Leo, D. (Dean) De, Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), and Leo, D. (Dean) De

Published
2016

35. MonetDB Jul2015-SP3 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), and Wits, A. (Abe)

Published
2016

36. MonetDB Jun2016 feature release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, Stalinov, S.G. (Svetlin), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Ferreira, P.E. (Pedro) Silva, and Stalinov, S.G. (Svetlin)

Published
2016

37. MonetDB Jul2015-SP4 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), Wits, A. (Abe), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Doehmen, T.R. (Till), Kruit, B.B. (Benno), and Wits, A. (Abe)

Published
2016

38. MonetDB Jul2015-SP2 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), Koutsourakis, P. (Panagiotis), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Raasveldt, M. (Mark), and Koutsourakis, P. (Panagiotis)

Published
2016

39. Managing big, linked, and open earth-observation data: Using the TELEIOS/LEO software stack

Author

Koubarakis, M. (Manolis), Kyzirakos, K. (Konstantinos), Nikolaou, C. (Charalampos), Garbis, G. (George), Bereta, K. (Konstantina), Dogani, R. (Roi), Giannakopoulou, S. (Stella), Smeros, P. (Panayiotis), Savva, D. (Dimitrianos), Stamoulis, G. (Georgios), Vlachopoulos, G. (Giannis), Manegold, S. (Stefan), Kontoes, C. (Charalampos), Herekakis, T. (Themistocles), Papoutsis, I. (Ioannis), Michail, D. (Dimitrios), Koubarakis, M. (Manolis), Kyzirakos, K. (Konstantinos), Nikolaou, C. (Charalampos), Garbis, G. (George), Bereta, K. (Konstantina), Dogani, R. (Roi), Giannakopoulou, S. (Stella), Smeros, P. (Panayiotis), Savva, D. (Dimitrianos), Stamoulis, G. (Georgios), Vlachopoulos, G. (Giannis), Manegold, S. (Stefan), Kontoes, C. (Charalampos), Herekakis, T. (Themistocles), Papoutsis, I. (Ioannis), and Michail, D. (Dimitrios)

Abstract

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service.

Published
2016
Full Text
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40. GeoTriples: a Tool for Publishing Geospatial Data as RDF Graphs Using R2RML Mappings

Author

Kyzirakos, K. (Konstantinos), Vlachopoulos, I. (Ioannis), Savva, D. (Dimitrianos), Manegold, S. (Stefan), Koubarakis, M. (Manolis), Kyzirakos, K. (Konstantinos), Vlachopoulos, I. (Ioannis), Savva, D. (Dimitrianos), Manegold, S. (Stefan), and Koubarakis, M. (Manolis)

Abstract

A plethora of Earth Observation data that is becoming available at no charge in Europe and the US recently reflects the strong push for more open Earth Observation data. Linked data is a paradigm which studies how one can make data available on the Web, and interconnect it with other data with the aim of making the value of the resulting “Web of data” greater than the sum of its parts. Open Earth Observation data that are currently made available by space agencies such as ESA and NASA are not following the linked data paradigm. Therefore, Earth Observation data and other kinds of geospatial data that are necessary for a user to satisfy her information needs can only be found in different data silos, where each silo may contain only part of the needed data. Publishing the content of these silos as RDF graphs, enables the development of data analytics applications with great environmental and financial value. In this paper we present the tool GeoTriples that allows for the transformation of Earth Observation data and geospatial data into RDF graphs. GeoTriples goes beyond the state of the art by extending the R2RML mapping language to be able to deal with the specificities of geospatial data. GeoTriples is a semi-automated tool that allows the publication of geospatial information into an RDF graph using the state of the art vocabularies like GeoSPARQL and stSPARQL, but at the same time it is not tightly coupled to a specific vocabulary.

Published
2015

41. The DBMS – your Big Data Sommelier

Author

Kargin, Y. (Yagiz), Kersten, M.L. (Martin), Manegold, S. (Stefan), Pirk, H. (Holger), Kargin, Y. (Yagiz), Kersten, M.L. (Martin), Manegold, S. (Stefan), and Pirk, H. (Holger)

Abstract

When addressing the problem of ``big'' data volume, preparation costs are one of the key challenges: the high costs for loading, aggregating and indexing data leads to a long data-to-insight time. In addition to being a nuisance to the end-user, this latency prevents real-time analytics on "big'' data. Fortunately, data often comes in semantic chunks such as files that contain data items that share some characteristics such as acquisition time or location. A data management system that exploits this trait can significantly lower the data preparation costs and the associated data-to-insight time by only investing in the preparation of the relevant chunks. In this paper, we develop such a system as an extension of an existing relational DBMS (MonetDB). To this end, we develop a query processing paradigm and data storage model that are partial-loading aware. The result is a system that can make a 1.2 TB dataset (consisting of 4000 chunks) ready for querying in less than 3 minutes on a single server-class machine while maintaining good query processing performance.

Published
2015
Full Text
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42. Holistic Indexing in Main-memory Column-stores

Author

Petraki, E. (Eleni), Idreos, S. (Stratos), Manegold, S. (Stefan), Petraki, E. (Eleni), Idreos, S. (Stratos), and Manegold, S. (Stefan)

Abstract

Great database systems performance relies heavily on index tuning, i.e., creating and utilizing the best indices depending on the workload. However, the complexity of the index tuning process has dramatically increased in recent years due to ad-hoc workloads and shortage of time and system resources to invest in tuning. This paper introduces holistic indexing, a new approach to automated index tuning in dynamic environments. Holistic indexing requires zero set-up and tuning effort, relying on adaptive index creation as a side-effect of query processing. Indices are created incrementally and partially; they are continuously refined as we process more and more queries. Holistic indexing takes the state-of-the-art adaptive indexing ideas a big step further by introducing the notion of a system which never stops refining the index space, taking educated decisions about which index we should incrementally refine next based on continuous knowledge acquisition about the running workload and resource utilization. When the system detects idle CPU cycles, it utilizes those extra cycles by refining the adaptive indices which are most likely to bring a benefit for future queries. Such idle CPU cycles occur when the system cannot exploit all available cores up to 100%, i.e., either because the workload is not enough to saturate the CPUs or because the current tasks performed for query processing are not easy to parallelize to the point where all available CPU power is exploited. In this paper, we present the design of holistic indexing for column-oriented database architectures and we discuss a detailed analysis against parallel versions of state-of-the-art indexing and adaptive indexing approaches. Holistic indexing is implemented in an open-source column-store DBMS. Our detailed experiments on both synthetic and standard benchmarks (TPC-H) and workloads (SkyServer) demonstrate that holistic indexing brings significant performance gains by being able to continuously refine the

Published
2015
Full Text
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43. Capturing the Laws of (Data) Nature

Author

Mühleisen, H.F. (Hannes), Kersten, M.L. (Martin), Manegold, S. (Stefan), Mühleisen, H.F. (Hannes), Kersten, M.L. (Martin), and Manegold, S. (Stefan)

Abstract

Model fitting is at the core of many scientific and industrial applications. These models encode a wealth of domain knowledge, something a database decidedly lacks. Except for simple cases, databases could not hope to achieve a deeper understanding of the hidden relationships in the data yet. We propose to harvest the statistical models that users fit to the stored data as part of their analysis and use them to advance physical data storage and approximate query answering to unprecedented levels of performance. We motivate our approach with an astronomical use case and discuss its potential.

Published
2015

44. Genome sequence analysis with MonetDB - A case study on Ebola virus diversity

Author

Cijvat, C.P. (Robin), Manegold, S. (Stefan), Kersten, M.L. (Martin), Klau, G.W. (Gunnar), Schönhuth, A. (Alexander), Marschall, T. (Tobias), Zhang, Y. (Ying), Cijvat, C.P. (Robin), Manegold, S. (Stefan), Kersten, M.L. (Martin), Klau, G.W. (Gunnar), Schönhuth, A. (Alexander), Marschall, T. (Tobias), and Zhang, Y. (Ying)

Abstract

Next-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but yields terabytes of data to be stored and analyzed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables \textit{easy}, \textit{flexible}, and \textit{rapid} management and analysis of sequence alignment data stored as Sequence Alignment/Map \\(SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virus \\genomes.

Published
2015

45. Genome sequence analysis with MonetDB: a case study on Ebola virus diversity

Author

Cijvat, C.P. (Robin), Manegold, S. (Stefan), Kersten, M.L. (Martin), Klau, G.W. (Gunnar), Schönhuth, A. (Alexander), Marschall, T. (Tobias), Zhang, Y. (Ying), Cijvat, C.P. (Robin), Manegold, S. (Stefan), Kersten, M.L. (Martin), Klau, G.W. (Gunnar), Schönhuth, A. (Alexander), Marschall, T. (Tobias), and Zhang, Y. (Ying)

Abstract

Next-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but results in terabytes of data to be stored and analysed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables easy, flexible, and rapid management and analysis of sequence alignment data stored as Sequence Alignment/Map (SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virus genomes.

Published
2015

46. MonetDB Oct2014-SP3 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, and Sidirourgos, E. (Eleftherios)

Published
2015

47. MonetDB Jul2015 feature release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, and Sidirourgos, E. (Eleftherios)

Published
2015

48. MonetDB Oct2014-SP4 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, and Sidirourgos, E. (Eleftherios)

Published
2015

49. MonetDB Oct2014-SP2 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pirk, H. (Holger), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Héman, S. (Sándor), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Ivanova, M.G. (Milena), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pirk, H. (Holger), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Héman, S. (Sándor), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), and Ivanova, M.G. (Milena)

Published
2015

50. MonetDB Jul2015-SP1 bugfix release

Author

Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), Koopmanschap, R.A. (Richard), Kersten, M.L. (Martin), Boncz, P.A. (Peter), Nes, N.J. (Niels), Manegold, S. (Stefan), Mullender, K.S. (Sjoerd), Rijke, J.A. (Arjen) de, Zhang, Y. (Ying), Pham, M.-D. (Minh-Duc), Scheers, L.H.A. (Bart), Petraki, E. (Eleni), Sellam, T.H.J. (Thibault), Kargin, Y. (Yagiz), Gawade, M.M. (Mrunal), Mühleisen, H.F. (Hannes), Kyzirakos, K. (Konstantinos), Nedev, D.G. (Dimitar), Cijvat, C.P. (Robin), Alvanaki, F. (Foteini), Dinther, M.H.M. (Martin) van, Sidirourgos, E. (Eleftherios), and Koopmanschap, R.A. (Richard)

Published
2015

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