Panagiotis Tampakis

Panagiotis Tampakis
University of Southern Denmark | SDU · Department of Mathematics and Computer Science

Doctor of Philosophy

About

27
Publications
1,373
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174
Citations
Citations since 2016
24 Research Items
168 Citations
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201620172018201920202021202201020304050
201620172018201920202021202201020304050
201620172018201920202021202201020304050

Publications

Publications (27)
Article
Full-text available
Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be...
Preprint
Full-text available
Wandering is a problematic behavior in people with dementia that can lead to dangerous situations. To alleviate this problem we design an approach for the real-time automatic detection of wandering leading to getting lost. The approach relies on GPS data to determine frequent locations between which movement occurs and a step that transforms GPS da...
Article
Full-text available
The i4sea research project provides effective and efficient big data integration, processing, and analysis technologies to deliver both real-time and historical operational snapshots of fishing vessels activity in national sea areas. This paper presents the architecture of the i4sea big data platform for sea area monitoring and analysis of fishing...
Preprint
Predictive analytics over mobility data are of great importance since they can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example of such analytics is future location prediction, where the goal is to predict the future location of a moving object,given a look-ahead time. What is even more chall...
Conference Paper
Predictive analytics over mobility data are of great importance since they can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example of such analytics is future location prediction, where the goal is to predict the future location of a moving object,given a look-ahead time. What is even more chall...
Chapter
The goal of mobility data analytics is to extract valuable knowledge out of a plethora of data sources that produce immense volumes of data. Focusing on the maritime domain, this relates to several challenging use-case scenarios, such as discovering valuable behavioural patterns of moving objects, identifying different types of activities in a regi...
Chapter
In recent years, there has been observed an “explosion” of trajectory data production due to the proliferation of GPS-enabled devices, such as mobile phones and tablets. This massive-scale data generation has posed new challenges in the data management community in terms of storing, querying, analyzing, and extracting knowledge out of such data. Kn...
Chapter
This chapter presents modern approaches and frameworks for predicting trajectories with detailed descriptions of three main research pillars. The first pillar is the problem formulation regarding two complementary tasks, namely the Future Location Prediction (FLP) and the Trajectory Prediction (TP). The second pillar tackles the issue of effectiven...
Article
Joining trajectory datasets is a significant operation in mobility data analytics and the cornerstone of various methods that aim to extract knowledge out of them. In the era of Big Data, the production of mobility data has become massive and, consequently, performing such an operation in a centralized way is not feasible. In this article, we addre...
Chapter
In this paper, we present a Big data framework for the prediction of streaming trajectory data by exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. In particular, to meet this goal we follow a two-step methodology. First, we efficiently identify the hidden mobility patterns in an offline manner. Su...
Conference Paper
Trajectory clustering is an important operation of knowledge discovery from mobility data. Especially nowadays, the need for performing advanced analytic operations over massively produced data, such as mobility traces, in efficient and scalable ways is imperative. However, discovering clusters of complete trajectories can overlook significant patt...
Thesis
The unprecedented rate of trajectory data generation that has been observed during the recent years, caused by the proliferation of GPS-enabled devices, poses new challenges in terms of storage, querying, analytics and knowledge extraction from mobility data. One of these challenges is cluster analysis, which aims at identifying clusters of moving...
Conference Paper
Full-text available
We present a big data framework for the prediction of streaming trajectory data, enriched from other data sources and exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. To meet this goal, we follow a multi-step methodology. First, we efficiently compress surveillance data in an online fashion, by co...
Preprint
Trajectory clustering is an important operation of knowledge discovery from mobility data. Especially nowadays, the need for performing advanced analytic operations over massively produced data, such as mobility traces, in efficient and scalable ways is imperative. However, discovering clusters of complete trajectories can overlook significant patt...
Preprint
Joining trajectory datasets is a significant operation in mobility data analytics and the cornerstone of various methods that aim to extract knowledge out of them. In the era of Big Data, the production of mobility data has become massive and, consequently, performing such an operation in a centralized way is not feasible. In this paper, we address...
Article
Full-text available
Cluster analysis over Moving Object Databases (MODs) is a challenging research topic that has attracted the attention of the mobility data mining community. In this paper, we study the temporal-constrained sub-trajectory cluster analysis problem, where the aim is to discover clusters of sub-trajectories given an ad-hoc, user-specified temporal cons...
Article
During the past few decades, a number of effective methods for indexing, query processing, and knowledge discovery in moving object databases have been proposed. An interesting research direction that has recently emerged handles semantics of movement instead of raw spatio-temporal data. Semantic annotations, such as “stop,” “move,” “at home,” “sho...
Conference Paper
In this paper, we present the overall architecture of RoadRunner, a Hadoop-based framework that enhances the efficiency of rank-aware query processing by introducing various optimizations to Hadoop, without changing its internal operation. RoadRunner focuses on a specific class of queries that involve ranking, such as top-k queries and top-k joins,...
Article
The domain of trajectory data management and mining undoubtedly contributes with interesting research problems and corresponding effective solutions to what is called data science. An interesting trend that poses new challenges in the field and has emerged especially due to the advance of location-based social networks, is that involved data cannot...
Conference Paper
During the last decade, the domain of mobility data mining has emerged providing many effective methods for the discovery of intuitive patterns representing collective behavior of trajectories of moving objects. Although a few real-world trajectory datasets have been made available recently, these are not sufficient for experimentally evaluating th...

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