Gerasimos Marketos | University of Piraeus (original) (raw)

Papers by Gerasimos Marketos

Research paper thumbnail of Mobility Data Warehousing and Mining

Very Large Data Bases, 2009

The usage of location aware devices, such as mobile phones and GPS-enabled devices, is widely spr... more The usage of location aware devices, such as mobile phones and GPS-enabled devices, is widely spread nowadays, allowing access to large spatiotemporal datasets. The space-time nature of this kind of data results in the generation of huge amounts of mobility data and imposes new challenges regarding the analytical tools that can be used for transforming raw data to knowledge. In our research, we investigate the extension of Data Warehousing and data mining technology so as to be applicable on mobility data. In this paper, we present the, so far, developed framework for analyzing mobility data and some preliminary results.

Research paper thumbnail of A Tool for Collecting, Querying and Mining Macroseismic Data

Bulletin of the Geological Society of Greece, 2004

SEISMO-SURFER is a tool for collecting, querying and mining seismic data being developed in Java ... more SEISMO-SURFER is a tool for collecting, querying and mining seismic data being developed in Java programming language using Oracle database system. The objective is to combine recent research trends and results in the fields of spatial and spatio-temporal databases, data warehouses and data mining, as well as well established visualization techniques for geographical information. The database of the tool is automatically updated from remote sources while existing possibilities allow the querying on different earthquakes parameters, the analysis of the data for extraction of useful information and the graphical representation of the results via maps, charts etc. In the present work, we extend SEISMO-SURFER to include macroseismic data collected by the Geodynamic Institute and filled in a relative database. More specifically, the seismic parameters of the strong earthquakes, stored into the SEISMO-SURFER database, are linked to the macroseismic intensities observed at different sites. Administrative information for each site, local surface geology, tectonic lines, damage photographs and detailed descriptions from newspapers are also included. University of Piraeus and Geodynamic Institute are working together to continuously update and develop SEISMO-SURFER, concerning the data included, the variety of parameters stored and the mining algorithms supported for exploiting knowledge.

Research paper thumbnail of A Framework for Integrating Ontologies and Pattern-Bases

IGI Global eBooks, Jan 18, 2011

Pattern Base Management Systems (PBMS) have been introduced as an effective way to manage the hig... more Pattern Base Management Systems (PBMS) have been introduced as an effective way to manage the high volume of patterns available nowadays. PBMS provide pattern management functionality in the same way where a Database Management System provides data management functionality. However, not all the extracted patterns are interesting; some are trivial and insignificant because they do not make sense according to the domain knowledge. Thus, in order to automate the pattern evaluation process, we need to incorporate the domain knowledge in it. We propose the integration of PBMS and Ontologies as a solution to the need of many scientific fields for efficient extraction of useful information from large databases and the exploitation of knowledge. In this chapter, we describe the potentiality of this integration and the issues that should be considered introducing an XML-based PBMS. We use a case study of data mining over scientific (seismological) data to illustrate the proposed PBMS and ontology integrated environment.

Research paper thumbnail of Seismological Data Warehousing and Mining

IGI Global eBooks, Jan 18, 2011

Earthquake data composes an ever increasing collection of earth science information for post-proc... more Earthquake data composes an ever increasing collection of earth science information for post-processing analysis. Earth scientists, local or national administration officers and so forth, are working with these data collections for scientific or planning purposes. In this article, we discuss the architecture of a so-called seismic data management and mining system (SDMMS) for quick and easy data collection, processing, and visualization. The SDMMS architecture includes, among others, a seismological database for efficient and effective querying and a seismological data warehouse for OLAP analysis and data mining. We provide template schemes for these two components as well as examples of their functionality towards the support of decision making. We also provide a comparative survey of existing operational or prototype SDMMS.

Research paper thumbnail of Seismic data management and mining systems – An Overview

We present the architecture of a so-called Seismic Data Management and Mining System (SDMMS) for ... more We present the architecture of a so-called Seismic Data Management and Mining System (SDMMS) for quick and easy data collection, processing, and visualization. The proposed SDMMS architecture includes, among others, a seismological database for efficient and effective querying and a seismological data warehouse for OLAP analysis and data mining. We provide template schemes for these two components as well as examples of their functionality. We also provide a survey of existing operational or prototype SDMMS.

Research paper thumbnail of Τεχνικές αποθήκευσης δεδομένων και εξόρυξης γνώσης για βάσεις κινούμενων αντικειμένων

Analyzing mobility data that are collected from location aware devices enables us to discover beh... more Analyzing mobility data that are collected from location aware devices enables us to discover behavioral patterns that can be explored in applications like service accessibility, mobile marketing and traffic management. Online analytical processing (OLAP) and data mining (DM) techniques can be employed in order to convert this vast amount of raw data into useful knowledge. Their application on conventional data has been extensively studied during the last decade. The high volume of generated mobility data arises the challenge of applying analytical techniques on such data. In order to achieve this aim, we have to take into consideration the complex nature of spatiotemporal data and thus to extend appropriately the two aforementioned techniques to handle them in an efficient way. This thesis proposes a framework for Mobility Data Warehousing and Mining which consists of various components (actually, Knowledge Discovery & Delivery steps). More specifically, Trajectory Data Warehousing techniques are addressed focusing on modeling issues, ETL processes (trajectory reconstruction, data cube loading) and OLAP operations (aggregation etc). Moreover, we propose data mining techniques that explore mobility data and extract a) interaction patterns for spatiotemporal representation, synthesis and classification and b) traffic patterns that can provide useful insights regarding the traffic flow on a road network.

Research paper thumbnail of Traffic mining in a road-network: How does the traffic flow?

International Journal of Business Intelligence and Data Mining, 2008

Research paper thumbnail of T-Warehouse: Visual OLAP analysis on trajectory data

Technological advances in sensing technologies and wireless telecommunication devices enable nove... more Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Research paper thumbnail of Building real-world trajectory warehouses

Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access - MobiDE '08, 2008

The flow of data generated from low-cost modern sensing technologies and wireless telecommunicati... more The flow of data generated from low-cost modern sensing technologies and wireless telecommunication devices enables novel research fields related to the management of this new kind of data and the implementation of appropriate analytics for knowledge extraction. In this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place when loading a moving object database with sampled location data originated e.g. from GPS recordings, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.

Research paper thumbnail of T-Warehouse: Visual OLAP analysis on trajectory data

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), 2010

Technological advances in sensing technologies and wireless telecommunication devices enable nove... more Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Research paper thumbnail of Mining Trajectory Databases via a Suite of Distance Operators

2007 IEEE 23rd International Conference on Data Engineering Workshop, 2007

With the rapid progress of mobile devices and positioning technologies, Trajectory Databases (TD)... more With the rapid progress of mobile devices and positioning technologies, Trajectory Databases (TD) have been in the core of database research during the last decade. Analysis and knowledge discovery in TD is an emerging field which has recently gained great interest. Extracting knowledge from TD using certain types of mining techniques, such as clustering and classification, impose that there is a mean to quantify the distance between two trajectories. Having as a main objective the support of effective similarity query processing, existing approaches utilize generic distance metrics that ignore the peculiarities of the trajectories as complex spatiotemporal data types. In this paper, we define a novel set of trajectory distance operators based on primitive (space and time) as well as derived parameters of trajectories (speed and direction). Aiming at providing a powerful toolkit for analysts who require producing distance matrices with different semantics as input to mining tasks, we develop algorithms for each of the proposed operators. The efficiency of our approach is evaluated through an experimental study on classification and clustering tasks using synthetic and real trajectory datasets.

Research paper thumbnail of Trajectory Collection and Reconstruction

Research paper thumbnail of Towards trajectory data warehouses

... the computation cost and as such the response time is prohibitive for either real-time servic... more ... the computation cost and as such the response time is prohibitive for either real-time services or ... c) The interpolated trajectory with the points matching the spatial and temporal minimum granularity Table 7.1 A simple fact table for a trajectory warehouse Time label X ...

Research paper thumbnail of Similarity search in Trajectory Databases

Proceedings of the International Workshop on Temporal Representation and Reasoning, 2007

Trajectory Database (TD) management is a relatively new topic of database research, which has eme... more Trajectory Database (TD) management is a relatively new topic of database research, which has emerged due to the explosion of mobile devices and positioning technologies. Trajectory similarity search forms an important class of queries in TD with applications in trajectory data analysis and spatiotemporal knowledge discovery. In contrast to related works which make use of generic similarity metrics that virtually ignore the temporal dimension, in this paper we introduce a framework consisting of a set of distance operators based on primitive (space and time) as well as derived parameters of trajectories (speed and direction). The novelty of the approach is not only to provide qualitatively different means to query for similar trajectories, but also to support trajectory clustering and classification mining tasks, which definitely imply a way to quantify the distance between two trajectories. For each of the proposed distance operators we devise highly parametric algorithms, the efficiency of which is evaluated through an extensive experimental study using synthetic and real trajectory datasets.

Research paper thumbnail of Visual Mobility Analysis using T-Warehouse

International Journal of Data Warehousing and Mining, 2011

Technological advances in sensing technologies and wireless telecommunication devices enable rese... more Technological advances in sensing technologies and wireless telecommunication devices enable research fields related to the management of trajectory data. The challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. In this paper, the authors demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, the authors implement T-Warehouse, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Research paper thumbnail of Analyzing Polls and News Headlines Using Business Intelligence Techniques

Opinion and market research companies gather a substantial amount of polls data which can be comb... more Opinion and market research companies gather a substantial amount of polls data which can be combined with news headlines, for the corresponding time periods they are collected. These data are analyzed in order to answer specific (predefined) questions related to the situation of each time period. However, when these tasks are fulfilled, the collected data are archived and possibly the majority of them will remain unutilized for future research. In this paper, we argue that these "inactive" data can be further analyzed and hidden knowledge can be extracted. For this reason, we propose an appropriate framework based on modern Business Intelligence (BI) techniques. The innovation of the proposed framework is that it is able to reuse and analyze data that have been collected in the past and discover hidden knowledge, which can be utilized to bring profit in many ways. The basic scope of our framework is a) to supply knowledge on trends regarding specific politico-social and m...

Research paper thumbnail of Visually exploring movement data via similarity-based analysis

Journal of Intelligent Information Systems, 2011

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns ... more Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit

Research paper thumbnail of GeoPKDD Deliverable D. 1.1. Privacy-aware Trajectory Warehouse Alignment Report

Citation: GeoPKDD Deliverable D. 1.1. Privacy-aware Trajectory Warehouse Alignment Report/ML Dami... more Citation: GeoPKDD Deliverable D. 1.1. Privacy-aware Trajectory Warehouse Alignment Report/ML Damiani, E. Frentzos, A. Gkoulalas-Divanis, D. Gougoulas, B. Kuijpers, J. Macedo, G. Marketos, A. Mazzoni, I. Ntoutsi, W. Othman, N. Pelekis, S. Puntoni, C. Renso, ...

Research paper thumbnail of Design of the Trajectory Warehouse Architecture. GeoPKDD Deliverable D. 1.3

Design of the Trajectory Warehouse Architecture. GeoPKDD Deliverable D.1.3. / ML Damiani, C. Vang... more Design of the Trajectory Warehouse Architecture. GeoPKDD Deliverable D.1.3. / ML Damiani, C. Vangenot, E. Frentzos, G. Marketos, I. Ntoutsi, N. Pelekis, Y. Theodoridis, V.Verykios, A. Rafaetta.. - Pisa : CNR, 2006. ... There are no files associated with this item.

Research paper thumbnail of A Framework for Trajectory Data Warehousing

Technological advances in sensing technologies and wireless telecommunication devices enable nove... more Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in data management world, the challenge after storing the data is the implementation of appropriate analytics that could extract useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place in order to transform sampled location data originated e.g. from GPS recordings into trajectories and load them to a moving object database, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings. location data producers Reconstructed trajectory data are stored in MOD Location data (x, y, t) are recorded MOD Trajectory reconstruction module Aggregates are loaded in the data cube (ETL procedure) Trajectory Data Cube trajectory data analyst Analysis over aggregate data is performed (OLAP) Figure 1. The architecture of our framework.

Research paper thumbnail of Mobility Data Warehousing and Mining

Very Large Data Bases, 2009

The usage of location aware devices, such as mobile phones and GPS-enabled devices, is widely spr... more The usage of location aware devices, such as mobile phones and GPS-enabled devices, is widely spread nowadays, allowing access to large spatiotemporal datasets. The space-time nature of this kind of data results in the generation of huge amounts of mobility data and imposes new challenges regarding the analytical tools that can be used for transforming raw data to knowledge. In our research, we investigate the extension of Data Warehousing and data mining technology so as to be applicable on mobility data. In this paper, we present the, so far, developed framework for analyzing mobility data and some preliminary results.

Research paper thumbnail of A Tool for Collecting, Querying and Mining Macroseismic Data

Bulletin of the Geological Society of Greece, 2004

SEISMO-SURFER is a tool for collecting, querying and mining seismic data being developed in Java ... more SEISMO-SURFER is a tool for collecting, querying and mining seismic data being developed in Java programming language using Oracle database system. The objective is to combine recent research trends and results in the fields of spatial and spatio-temporal databases, data warehouses and data mining, as well as well established visualization techniques for geographical information. The database of the tool is automatically updated from remote sources while existing possibilities allow the querying on different earthquakes parameters, the analysis of the data for extraction of useful information and the graphical representation of the results via maps, charts etc. In the present work, we extend SEISMO-SURFER to include macroseismic data collected by the Geodynamic Institute and filled in a relative database. More specifically, the seismic parameters of the strong earthquakes, stored into the SEISMO-SURFER database, are linked to the macroseismic intensities observed at different sites. Administrative information for each site, local surface geology, tectonic lines, damage photographs and detailed descriptions from newspapers are also included. University of Piraeus and Geodynamic Institute are working together to continuously update and develop SEISMO-SURFER, concerning the data included, the variety of parameters stored and the mining algorithms supported for exploiting knowledge.

Research paper thumbnail of A Framework for Integrating Ontologies and Pattern-Bases

IGI Global eBooks, Jan 18, 2011

Pattern Base Management Systems (PBMS) have been introduced as an effective way to manage the hig... more Pattern Base Management Systems (PBMS) have been introduced as an effective way to manage the high volume of patterns available nowadays. PBMS provide pattern management functionality in the same way where a Database Management System provides data management functionality. However, not all the extracted patterns are interesting; some are trivial and insignificant because they do not make sense according to the domain knowledge. Thus, in order to automate the pattern evaluation process, we need to incorporate the domain knowledge in it. We propose the integration of PBMS and Ontologies as a solution to the need of many scientific fields for efficient extraction of useful information from large databases and the exploitation of knowledge. In this chapter, we describe the potentiality of this integration and the issues that should be considered introducing an XML-based PBMS. We use a case study of data mining over scientific (seismological) data to illustrate the proposed PBMS and ontology integrated environment.

Research paper thumbnail of Seismological Data Warehousing and Mining

IGI Global eBooks, Jan 18, 2011

Earthquake data composes an ever increasing collection of earth science information for post-proc... more Earthquake data composes an ever increasing collection of earth science information for post-processing analysis. Earth scientists, local or national administration officers and so forth, are working with these data collections for scientific or planning purposes. In this article, we discuss the architecture of a so-called seismic data management and mining system (SDMMS) for quick and easy data collection, processing, and visualization. The SDMMS architecture includes, among others, a seismological database for efficient and effective querying and a seismological data warehouse for OLAP analysis and data mining. We provide template schemes for these two components as well as examples of their functionality towards the support of decision making. We also provide a comparative survey of existing operational or prototype SDMMS.

Research paper thumbnail of Seismic data management and mining systems – An Overview

We present the architecture of a so-called Seismic Data Management and Mining System (SDMMS) for ... more We present the architecture of a so-called Seismic Data Management and Mining System (SDMMS) for quick and easy data collection, processing, and visualization. The proposed SDMMS architecture includes, among others, a seismological database for efficient and effective querying and a seismological data warehouse for OLAP analysis and data mining. We provide template schemes for these two components as well as examples of their functionality. We also provide a survey of existing operational or prototype SDMMS.

Research paper thumbnail of Τεχνικές αποθήκευσης δεδομένων και εξόρυξης γνώσης για βάσεις κινούμενων αντικειμένων

Analyzing mobility data that are collected from location aware devices enables us to discover beh... more Analyzing mobility data that are collected from location aware devices enables us to discover behavioral patterns that can be explored in applications like service accessibility, mobile marketing and traffic management. Online analytical processing (OLAP) and data mining (DM) techniques can be employed in order to convert this vast amount of raw data into useful knowledge. Their application on conventional data has been extensively studied during the last decade. The high volume of generated mobility data arises the challenge of applying analytical techniques on such data. In order to achieve this aim, we have to take into consideration the complex nature of spatiotemporal data and thus to extend appropriately the two aforementioned techniques to handle them in an efficient way. This thesis proposes a framework for Mobility Data Warehousing and Mining which consists of various components (actually, Knowledge Discovery & Delivery steps). More specifically, Trajectory Data Warehousing techniques are addressed focusing on modeling issues, ETL processes (trajectory reconstruction, data cube loading) and OLAP operations (aggregation etc). Moreover, we propose data mining techniques that explore mobility data and extract a) interaction patterns for spatiotemporal representation, synthesis and classification and b) traffic patterns that can provide useful insights regarding the traffic flow on a road network.

Research paper thumbnail of Traffic mining in a road-network: How does the traffic flow?

International Journal of Business Intelligence and Data Mining, 2008

Research paper thumbnail of T-Warehouse: Visual OLAP analysis on trajectory data

Technological advances in sensing technologies and wireless telecommunication devices enable nove... more Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Research paper thumbnail of Building real-world trajectory warehouses

Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access - MobiDE '08, 2008

The flow of data generated from low-cost modern sensing technologies and wireless telecommunicati... more The flow of data generated from low-cost modern sensing technologies and wireless telecommunication devices enables novel research fields related to the management of this new kind of data and the implementation of appropriate analytics for knowledge extraction. In this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place when loading a moving object database with sampled location data originated e.g. from GPS recordings, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.

Research paper thumbnail of T-Warehouse: Visual OLAP analysis on trajectory data

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), 2010

Technological advances in sensing technologies and wireless telecommunication devices enable nove... more Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-WAREHOUSE, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Research paper thumbnail of Mining Trajectory Databases via a Suite of Distance Operators

2007 IEEE 23rd International Conference on Data Engineering Workshop, 2007

With the rapid progress of mobile devices and positioning technologies, Trajectory Databases (TD)... more With the rapid progress of mobile devices and positioning technologies, Trajectory Databases (TD) have been in the core of database research during the last decade. Analysis and knowledge discovery in TD is an emerging field which has recently gained great interest. Extracting knowledge from TD using certain types of mining techniques, such as clustering and classification, impose that there is a mean to quantify the distance between two trajectories. Having as a main objective the support of effective similarity query processing, existing approaches utilize generic distance metrics that ignore the peculiarities of the trajectories as complex spatiotemporal data types. In this paper, we define a novel set of trajectory distance operators based on primitive (space and time) as well as derived parameters of trajectories (speed and direction). Aiming at providing a powerful toolkit for analysts who require producing distance matrices with different semantics as input to mining tasks, we develop algorithms for each of the proposed operators. The efficiency of our approach is evaluated through an experimental study on classification and clustering tasks using synthetic and real trajectory datasets.

Research paper thumbnail of Trajectory Collection and Reconstruction

Research paper thumbnail of Towards trajectory data warehouses

... the computation cost and as such the response time is prohibitive for either real-time servic... more ... the computation cost and as such the response time is prohibitive for either real-time services or ... c) The interpolated trajectory with the points matching the spatial and temporal minimum granularity Table 7.1 A simple fact table for a trajectory warehouse Time label X ...

Research paper thumbnail of Similarity search in Trajectory Databases

Proceedings of the International Workshop on Temporal Representation and Reasoning, 2007

Trajectory Database (TD) management is a relatively new topic of database research, which has eme... more Trajectory Database (TD) management is a relatively new topic of database research, which has emerged due to the explosion of mobile devices and positioning technologies. Trajectory similarity search forms an important class of queries in TD with applications in trajectory data analysis and spatiotemporal knowledge discovery. In contrast to related works which make use of generic similarity metrics that virtually ignore the temporal dimension, in this paper we introduce a framework consisting of a set of distance operators based on primitive (space and time) as well as derived parameters of trajectories (speed and direction). The novelty of the approach is not only to provide qualitatively different means to query for similar trajectories, but also to support trajectory clustering and classification mining tasks, which definitely imply a way to quantify the distance between two trajectories. For each of the proposed distance operators we devise highly parametric algorithms, the efficiency of which is evaluated through an extensive experimental study using synthetic and real trajectory datasets.

Research paper thumbnail of Visual Mobility Analysis using T-Warehouse

International Journal of Data Warehousing and Mining, 2011

Technological advances in sensing technologies and wireless telecommunication devices enable rese... more Technological advances in sensing technologies and wireless telecommunication devices enable research fields related to the management of trajectory data. The challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. In this paper, the authors demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, the authors implement T-Warehouse, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis on mobility data.

Research paper thumbnail of Analyzing Polls and News Headlines Using Business Intelligence Techniques

Opinion and market research companies gather a substantial amount of polls data which can be comb... more Opinion and market research companies gather a substantial amount of polls data which can be combined with news headlines, for the corresponding time periods they are collected. These data are analyzed in order to answer specific (predefined) questions related to the situation of each time period. However, when these tasks are fulfilled, the collected data are archived and possibly the majority of them will remain unutilized for future research. In this paper, we argue that these "inactive" data can be further analyzed and hidden knowledge can be extracted. For this reason, we propose an appropriate framework based on modern Business Intelligence (BI) techniques. The innovation of the proposed framework is that it is able to reuse and analyze data that have been collected in the past and discover hidden knowledge, which can be utilized to bring profit in many ways. The basic scope of our framework is a) to supply knowledge on trends regarding specific politico-social and m...

Research paper thumbnail of Visually exploring movement data via similarity-based analysis

Journal of Intelligent Information Systems, 2011

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns ... more Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit

Research paper thumbnail of GeoPKDD Deliverable D. 1.1. Privacy-aware Trajectory Warehouse Alignment Report

Citation: GeoPKDD Deliverable D. 1.1. Privacy-aware Trajectory Warehouse Alignment Report/ML Dami... more Citation: GeoPKDD Deliverable D. 1.1. Privacy-aware Trajectory Warehouse Alignment Report/ML Damiani, E. Frentzos, A. Gkoulalas-Divanis, D. Gougoulas, B. Kuijpers, J. Macedo, G. Marketos, A. Mazzoni, I. Ntoutsi, W. Othman, N. Pelekis, S. Puntoni, C. Renso, ...

Research paper thumbnail of Design of the Trajectory Warehouse Architecture. GeoPKDD Deliverable D. 1.3

Design of the Trajectory Warehouse Architecture. GeoPKDD Deliverable D.1.3. / ML Damiani, C. Vang... more Design of the Trajectory Warehouse Architecture. GeoPKDD Deliverable D.1.3. / ML Damiani, C. Vangenot, E. Frentzos, G. Marketos, I. Ntoutsi, N. Pelekis, Y. Theodoridis, V.Verykios, A. Rafaetta.. - Pisa : CNR, 2006. ... There are no files associated with this item.

Research paper thumbnail of A Framework for Trajectory Data Warehousing

Technological advances in sensing technologies and wireless telecommunication devices enable nove... more Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in data management world, the challenge after storing the data is the implementation of appropriate analytics that could extract useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place in order to transform sampled location data originated e.g. from GPS recordings into trajectories and load them to a moving object database, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings. location data producers Reconstructed trajectory data are stored in MOD Location data (x, y, t) are recorded MOD Trajectory reconstruction module Aggregates are loaded in the data cube (ETL procedure) Trajectory Data Cube trajectory data analyst Analysis over aggregate data is performed (OLAP) Figure 1. The architecture of our framework.