Apostolos Glenis's research while affiliated with University of Piraeus and other places
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Publications (12)
Transforming disparate and heterogeneous data sources that provide large volumes of data in high velocity into a common form allows integrated and enriched views on data and thus provides further opportunities to advance the effectiveness and accuracy of data analysis and prediction tasks. This paper presents the RDF-Gen approach for transforming d...
In this demonstration, we present DatAgent , an intelligent data assistant system that allows users to ask queries in natural language, and can respond in natural language as well. Moreover, the system actively guides the user using different types of recommendations and hints, and learns from user actions. We will demonstrate different exploration...
More and more real-life applications for mobility analytics require the joint exploitation of positional information of moving objects together with weather data that correspond to the movement. In particular, this is evident in fleet management applications for improved routing and reduced fuel consumption, in the maritime domain for more accurate...
As big data sources providing time series increase, and data is provided in increased velocity and volume, we need to efficiently recognize data provided, classifying it according to their type, origin etc. This is a first important step in doing analytics on data provided from disparate data sources, such as archival sources, multiple sensors, or...
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...
In this paper, we present the design and implementation of a link discovery (LD) framework targeting spatial and spatio-temporal data. Existing works are either very specific (focusing on limited spatial LD tasks), or even though being generic LD frameworks, they do not support spatial nor spatio-temporal relations. Motivated by such limitations, w...
An ever-increasing number of real-life applications produce spatiotemporal data that record the position of moving objects (persons, cars, vessels, aircrafts, etc.). In order to provide integrated views with other relevant data sources (e.g., weather, vessel databases, etc.), this data is represented in RDF and stored in knowledge bases with the fo...
An ever-increasing number of applications in critical domains, such as maritime and aviation, generate, collect, manage and process spatio-temporal data related to the mobility of entities. This wealth of data can be exploited for various purposes, towards improving the safety of operations, reducing economical costs, and increasing dependability:...
Motivated by real-life emerging needs in critical domains, this paper proposes a coherent and generic ontology for the representation of semantic trajectories, in association with related events and contextual information. The main contribution of the proposed ontology is the representation of semantic trajectories at different levels of spatio-tem...
Citations
... While some of the systems presented in this work can be adapted to work in a conversational setting, heavier modifications are often necessary in order for the model to effectively encode the conversation history and the previous SQL predictions (note that we have only discussed about encoding NL and DB schemas). Ultimately, this aspect of the problem opens the path towards "intelligent data assistants" [64], similar to but extremely more powerful than the intelligent personal assistants that are gaining more and more popularity and use through our smartphones and dedicated speakers devices. ...
... Query recommendations Even when the user understands the data that is kept in the database, it might not always be clear what kind of queries can be asked and what kind of knowledge can be extracted. For this reason, query recommendations can help a user find interesting queries to ask the database, either based on the user preferences and history, or on queries that are frequently asked by other users of the same database [41] or by analysing the data [31]. In this context, adapting deep-learning models for query recommendations offers numerous challenges and opportunities. ...
... Existing databases and spatial data libraries can thus be used for implementing the analysis [13,14]. In [15] the proposed system enriches the individual location points with weather information. The points are processed independently from each other, where the system uses an external source storing weather data to enrich specific positions by weather attributes. ...
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... What is even more challenging is predictive analytics over mobility data, where the goal is to predict the future behaviour of moving objects, which can have a wide range of applications, such as predicting collisions, future encounters, traffic jams, etc. At an individual level, a typical and well-studied example of such analytics is future location prediction [11,24,25,29,33], where the goal is to predict the future location of a moving object, given a look-ahead time. However, prediction of future mobility behaviour at a collective level has not been addressed at the degree of its individual counterpart. ...
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... However, no open-source system organizes these algorithms into a common framework so as to facilitate researchers and practitioners in their effort to populate the LOD cloud with more topological relations. Systems like Silk [19] and LIMES [25] convey only the methods developed by their creators, Silk-spatial [38] and RADON [36] respectively, while systems that could act as a library of established methods, such as stLD [33,34], are not publicly available. Moreover, no system supports progressive methods, neither for serial nor for parallel processing, even though they are indispensable for applications with limited computational or temporal resources [28]. ...
... In this section of the RDF file, we created a Universal Resource Identifier (URI) for each Scientometric Indicator. The object can also be identified by a literal for the representation of simple values such as strings or numbers [25]. An example is shown in Fig. 3. ...
... Such semantically enriched trajectories are called semantic trajectories (STs) [14]. SotA research in the field of ST management and analysis [15,16] provides useful information about human (pedestrians) [17][18][19] and nonhuman (vehicle) [20,21] movement. One of the key challenges is to recognize and interlink movement patterns and correlate them with entities' movement behaviors to extract and infer new related knowledge. ...
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... The segments of an object's movement, which have been defined based on the interest that they present for a specific application, e.g., UAV movement within a specific area for a given recording/documentation mission, are called trajectories of the moving object [1]. A trajectory can be (a) enriched with additional data (beyond latitude, longitude, timestamp), and/or (b) enhanced with complementary segmentations, constituting a semantic trajectory [2]. Annotations for the segmented parts of a trajectory (episodes) could be "stop" or "move", or in other cases recordings of POIs or ROIs. ...
