This paper addresses short-term Collision-Risk-Aware ship route planning while utilizing a deep learning-based Vessel Collision Risk Assessment and Forecasting (VCRA/F) framework to quantify risks. Lacking a clear boundary between risky and viable routes, we propose a Pareto-optimal search for alternative routes, balancing collision risk and voyage time. Our main contribution is a novel framework that integrates VCRA/F for Pareto-optimal route queries in dynamic environments. We model maritime routes using a hexagon-based graph network on the sea. Our experiments on real-world AIS data validate the effectiveness of Skyline-VCRA/F while highlighting areas for further improvement.

The wide spread of Automatic Identification System (AIS) has motivated several maritime analytics operations. Vessel Location Forecasting (VLF) is one of the most critical operations for maritime awareness. However, accurate VLF is a challenging problem due to the complexity and dynamic nature of maritime traffic conditions. Furthermore, as privacy concerns and restrictions have grown, training data has become increasingly fragmented, resulting in dispersed databases of several isolated data silos among different organizations, which in turn decreases the quality of learning models. In this paper, we propose an efficient VLF solution based on LSTM neural networks, in two variants, namely Nautilus and FedNautilus for the centralized and the federated learning approach, respectively. We also demonstrate the superiority of the centralized approach with respect to current state of the art and discuss the advantages and disadvantages of the federated against the centralized approach.

The wide spread of the Automatic Identification System (AIS) and related tools has motivated several maritime analytics operations. One of the most critical operations for the purpose of maritime safety is the so-called Vessel Collision Risk Assessment and Forecasting (VCRA/F), with the difference between the two lying in the time horizon when the collision risk is calculated: either at current time by assessing the current collision risk (i.e., VCRA) or in the (near) future by forecasting the anticipated locations and corresponding collision risk (i.e., VCRF). Accurate VCRA/F is a difficult task, since maritime traffic can become quite volatile due to various factors, including weather conditions, vessel manoeuvres, etc. Addressing this problem by using complex models introduces a trade-off between accuracy (in terms of quality of assessment / forecasting) and responsiveness. In this paper, we propose a deep learning-based framework that discovers encountering vessels and assesses/predicts their corresponding collision risk probability, in the latter case via state-of-the-art vessel route forecasting methods. Our experimental study on a real-world AIS dataset demonstrates that the proposed framework balances the aforementioned trade-off while presenting up to 70% improvement in R2 score, with an overall accuracy of around 96% for VCRA and 77% for VCRF.

The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data. One of these challenges is online maritime awareness, where the goal is to monitor and ensure the safety of a fleet, including, among others, collision risk assessment. To address this challenge, we present VesselVision, a system that estimates, tracks, and visualizes vessels' collision risk. In particular, our system offers a unified solution that tracks vessels that are detected to be in encountering process and assess their corresponding collision risk over streaming AIS position data in an online fashion. The functionality of our system is demonstrated over popular real-world AIS datasets.

Maritime transport systems are essential to human mobility. A vital part of the maritime transport systems is the accurate vessel route forecasting (VRF). However, accurate VRF is a challenging task due to the fact that maritime traffic conditions are complex and dynamic. Machine learning (ML) methods can leverage from the ”explosion” of vessel surveillance information in order to encourage, enable deeper digitalization in the shipping industries and tackle the VRF problem. In this paper we investigate some of the most popular ML methods to address the VRF problem and we present an overview of these methods through an experimental testbed based on real vessel surveillance data. This work results in introducing baseline ML models for VRF purposes.

Within the last years the shipping industry invest-ments continue to grow to improve maritime transport systems. A vital part of the maritime transport systems is the accurate Vessel Traffic Flow Forecasting (VTFF). In this paper, we approach the VTFF problem from two different perspectives: a) indirect - as a vessel route forecasting application via employing predicted vessels locations in the future, and b) direct - as a flow sequence forecasting problem. In both strategies, machine learning methods are employed because they can leverage from the massive vessel surveillance information to enable deeper digitalization in the shipping industry. This work performs an experimental comparative study between the two approaches over a real dataset from the maritime domain.

The wide spread of Automatic Identification System (AIS) and tools based on it has motivated several maritime analytics operations. One of the most critical operations for the purpose of maritime safety is the so-called Vessel Collision Risk Assessment (VCRA). Accurate VCRA is a challenging task as maritime traffic is quite volatile, often affected by external factors, such as weather, etc. Addressing this problem by using complex models introduces a trade-off between accuracy quality and responsiveness. On the other hand, Machine Learning (ML) methods can better address this tradeoff. In this paper, we study the VCRA problem from the ML perspective, by proposing an architecture based on the Multi-Layered Perceptron (MLP) model. Our preliminary experimental study over a large-scale AIS dataset shows that the proposed methodology outperforms the kinematic equations-based approach.

The increasing use of devices with GPS capabilities has raised the need for storing and managing large amounts of spatio-temporal data, which can then be used by appropriate services and applications for extracting useful information from movement data. In parallel, it introduced the concept of multiple aspect trajectories that combine spatial, temporal, textual and social information in tandem. In order to capitalize on the social aspect of these movement data (specifically for social rankings), we formulate and address the problem of Social Spatio-Temporal-Keyword Pattern (S²KP) search over multiple aspect trajectory databases (MATDs). We propose an efficient in-DBMS k-d tree-based integrated index solution for multiple aspect trajectories that takes into account the sequential nature of trajectory data and a pattern search algorithm for this query type, implemented in Neo4j - a NoSQL graph DBMS. The overall search framework supports either an index-based spatial filtering first and then a social filtering based on social ranking and keywords, or vice versa, depending on a word frequency list. The efficacy of our proposal is demonstrated with an extensive evaluation over a real and a synthetic dataset.

In this demo paper we present ST_VISIONS, an easy-to-use Python library for interactive visualizations of spatial and spatio-temporal datasets. By automating the low-level details of the underlying visualization library (Bokeh), ST_VISIONS allows data scientists to create interactive, map-based visualizations, by writing Python code at a higher level of abstraction. Consequently, we accelerate the task of visualization from different sources, while we support interactive filtering, colorization, as well as multiple graphs, for various types of spatial and spatio-temporal data.

Spatial-keyword queries are important for a wide range of applications that retrieve data based on a combination of keyword search and spatial constraints. However, efficient processing of spatial-keyword queries is not a trivial task because the combination of textual and spatial data results in a high-dimensional representation that is challenging to index effectively. To address this problem, in this paper, we propose a novel indexing scheme for efficient support of spatial-keyword range queries. At the heart of our approach lies a carefully-designed mapping of spatio-textual data to a two-dimensional (2D) space that produces compact partitions of spatio-textual data. In turn, the mapped 2D data can be indexed effectively by traditional spatial data structures, such as an R-tree. We propose bounds, theoretically proven for correctness, that lead to the design of a filter-and-refine algorithm that prunes the search space effectively. In this way, our approach for spatial-keyword range queries is readily applicable to any database system that provides spatial support. In our experimental evaluation, we demonstrate how our algorithm can be implemented over PostgreSQL and exploit its underlying spatial index provided by PostGIS, in order to process spatial-keyword range queries efficiently. Moreover, we show that our solution outperforms different competitor approaches.

The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data. One of these challenges is online cluster analysis, where the goal is to unveil hidden patterns of collective behaviour from streaming trajectories, such as co-movement and co-stationary (aka anchorage) patterns. Towards this direction, in this paper, we demonstrate MaSEC (Moving and Stationary Evolving Clusters), a system that discovers valuable behavioural patterns as above. In particular, our system provides a unified solution that discovers both moving and stationary evolving clusters on streaming vessel position data in an online mode. The functionality of our system is evaluated over two real-world datasets from the maritime domain.

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 patterns that exist only for a small portion of their lifespan. In this paper, we address the problem of Distributed Subtrajectory Clustering in an efficient and highly scalable way. The problem is challenging because the subtrajectories to be clustered are not known in advance, but they need to be discovered dynamically based on adjacent subtrajectories in space and time. Towards this objective, we split the original problem to three sub-problems, namely Subtrajectory Join, Trajectory Segmentation and Clustering and Outlier Detection, and deal with each one in a distributed fashion by utilizing the MapReduce programming model. The efficiency and the effectiveness of our solution is demonstrated experimentally over a synthetic and two large real datasets from the maritime and urban domains and through comparison with two state of the art subtrajectory clustering algorithms.

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 constructing trajectory synopses that are spatio-temporally linked with streaming and archival data from a variety of diverse and heterogeneous data sources. The enriched stream of trajectory synopses is stored in a distributed RDF store, supporting data exploration via SPARQL queries. The enriched stream of synopses along with the raw data is consumed by trajectory prediction algorithms that exploit mined patterns from the RDF store, namely medoids of (sub-) trajectory clusters, which prolong the horizon of useful predictions. The framework is extended with offline and online interactive visual analytics tool to facilitate real world analysis in the maritime and the aviation domains.

Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of hot spot analysis for massive trajectory data of moving objects, which has many real-life applications in different domains, especially in the analysis of vast repositories of historical traces of spatio-temporal data (cars, vessels, aircrafts). In order to identify hot spots, we propose an approach that relies on the Getis-Ord statistic, which has been used successfully in the past for point data. Since trajectory data is more than just a collection of individual points, we formulate the problem of trajectory hot spot analysis, using the Getis-Ord statistic. We propose a parallel and scalable algorithm for this problem, called THS, which provides an exact solution and can operate on vast-sized data sets. Moreover, we introduce an approximate algorithm (aTHS) that avoids exhaustive computation and trades-off accuracy for efficiency in a controlled manner. In essence, we provide a method that quantifies the maximum induced error in the approximation, in relation with the achieved computational savings. We develop our algorithms in Apache Spark and demonstrate the scalability and efficiency of our approach using a large, historical, real-life trajectory data set of vessels sailing in the Eastern Mediterranean for a period of three years.

In this paper, we demonstrate Hermes@Neo4j1, an extension of Neo4j graph DMBS for semantic trajectories of moving objects, on the so-called Spatio-Temporal-Keyword Pattern queries. For this purpose, our engine exploits on hybrid Spatio-Temporal- Keyword (STK) index structures, also boosted by an appropriate selectivity estimation model. Hermes@Neo4j functionality is demonstrated over synthetic and real semantic trajectory datasets.

We present an application framework that consumes streaming positions from a large fleet of flying aircrafts monitored in real time over a wide geographical area. Tailored for aviation surveillance, this online processing scheme only retains locations conveying salient mobility events along each flight, and annotates them as stop, change of speed, heading or altitude, etc. Such evolving trajectory synopses must keep in pace with the incoming raw streams so as to get incrementally annotated with minimal loss in accuracy. We also develop one-pass heuristics to eliminate inherent noise and provide reliable trajectory representations. Our prototype implementation on top of Apache Flink and Kafka has been tested against various real and synthetic datasets offering concrete evidence of its timeliness, scalability, and compression efficiency, with tolerable concessions to the quality of resulting trajectory approximations.

In this paper, we present an efficient in-DBMS framework for progressive time-aware sub-trajectory cluster analysis. In particular, we address two variants of the problem: (a) spatiotemporal sub-trajectory clustering and (b) index-based time-aware clustering at querying environment. Our approach for (a) relies on a two-phase process: a voting-and-segmentation phase followed by a sampling-and-clustering phase. Regarding (b), we organize data into partitions that correspond to groups of sub-trajectories, which are incrementally maintained in a hierarchical structure. Both approaches have been implemented in Hermes@PostgreSQL, a real Moving Object Database engine built on top of PostgreSQL, enabling users to perform progressive cluster analysis via simple SQL. The framework is also extended with a Visual Analytics (VA) tool to facilitate real world analysis.

Trajectory data analysis and mining require distance and similarity measures, and the quality of their results is directly related to those measures. Several similarity measures originally proposed for time-series were adapted to work with trajectory data, but these approaches were developed for well-behaved data that usually do not have the uncertainty and heterogeneity introduced by the sampling process to obtain trajectories. More recently, similarity measures were proposed specifically for trajectory data, but they rely on simplistic movement uncertainty representations, such as linear interpolation. In this article, we propose a new distance function, and a new similarity measure that uses an elliptical representation of trajectories, being more robust to the movement uncertainty caused by the sampling rate and the heterogeneity of this kind of data. Experiments using real data show that our proposal is more accurate and robust than related work.

In this paper, we propose an efficient in-DBMS solution for the problem of sub-trajectory clustering and outlier detection in large moving object datasets. The method relies on a two-phase process: a voting-and-segmentation phase that segments trajectories according to a local density criterion and trajectory similarity criteria, followed by a sampling-and-clustering phase that selects the most representative sub-trajectories to be used as seeds for the clustering process. Our proposal, called S 2 T-Clustering (for Sampling-based Sub-Trajectory Clustering) is novel since it is the first, to our knowledge, that addresses the pure spatiotemporal sub-trajectory clustering and outlier detection problem in a real-world setting (by ‘pure’ we mean that the entire spatiotemporal information of trajectories is taken into consideration). Moreover, our proposal can be efficiently registered as a database query operator in the context of extensible DBMS (namely, PostgreSQL in our current implementation). The effectiveness and the efficiency of the proposed algorithm are experimentally validated over synthetic and real-world trajectory datasets, demonstrating that S 2 T-Clustering outperforms an off-the-shelf in-DBMS solution using PostGIS by several orders of magnitude.

The preservation of privacy when publishing spatiotemporal traces of mobile humans is a field that is receiving growing attention. However, while more and more services offer personalized privacy options to their users, few trajectory anonymization algorithms are able to handle personalization effectively, without incurring unnecessary information distortion. In this paper, we study the problem of Personalized (K,�)anonymity , which builds upon the model of (k,δ)-anonymity, while allowing users to have their own individual privacy and service quality requirements. First, we propose efficient modifications to state-of-the-art (k,δ)-anonymization algorithms by introducing a novel technique built upon users’ personalized privacy settings. This way, we avoid over-anonymization and we decrease information distortion. In addition, we utilize datasetaware trajectory segmentation in order to further reduce information distortion. We also study the novel problem of

Predominant smartphone OS localization subsystems currently rely on server-side localization processes, allowing the service provider to know the location of a user at all times. In this paper, we propose an innovative algorithm for protecting users from location tracking by the localization service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our proposed Temporal Vector Map (TVM) algorithm, allows a user to accurately localize by exploiting a k-Anonymity Bloom (kAB) filter and a bestNeighbors generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. We have evaluated our framework using a real prototype developed in Android and Hadoop HBase as well as realistic Wi-Fi traces scaling-up to several GBs. Our study reveals that TVM can offer fine-grained localization in approximately four orders of magnitude less energy and number of messages than competitive approaches.

Maritime monitoring is a typical Big Data problem where hundreds of thousands of vessels across the globe transmit messages about their location, speed and other information. We have developed a system for online vessel tracking that performs, as a first step, a high-rate but accurate trajectory compression. Subsequently, the compressed trajectories are analyzed by a complex event recognition engine, promptly reporting alerts to maritime authorities. To deal with realistic maritime event patterns, we seamlessly integrated spatial and temporal reasoning for online event recognition. The system is evaluated on real data from the Greek seas.

The explosion of available positioning information associated with the inferred or user-declared semantics of the respective locations, already contributes in what is called the big data era, posing new challenges to the mobility data management and mining research community. In this paper, motivated by a series of challenges set in [11], we present a unified framework for the management and the analysis of our LifeSteps, i.e. data objects that include both (raw) trajectories and their semantic counterpart. In particular, we provide solutions for developing real-world semantic-aware Moving Object Database (MOD) and Trajectory Data Warehouse (TDW) systems and we devise respective query processing algorithms. Our experimental study on synthetic data including synchronized raw (i.e., GPS log) and semantic (i.e., diaries) information, verifies the effectiveness and efficiency of the proposed framework.

We present a system that combines intelligent online tracking with complex event recognition against streaming positions relayed from numerous vessels. Given the vital importance of maritime safety to the environment, the economy, and in national security, our sys-tem leverages the real-time acquisition of vessel activity with ge-ographical and other static information. Thus, it can offer timely notification in emergency situations, such as intrusion into marine preservation areas, loitering, and unsafe sailing. Thanks to a mobil-ity tracking module, evolving trajectories generated by massive po-sitional updates can be compressed online into concise, but reliable synopses per ship, retaining only salient motion features within a sliding window. These features are exploited by a complex event recognition module that detects suspicious situations of interest to maritime authorities. We conducted a comprehensive empirical validation against a real dataset of traces collected from thousands of vessels. Our results confirm the scalability and approximation accuracy of the proposed system, and thus demonstrate its poten-tial for effective, real-time maritime monitoring. 1.

In this paper we present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is time-dependent and a given ordered list of categories of interest, we find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest. Our approach uses the A* search paradigm equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. Our experiments using a real data set have shown our proposed solution to be up to two orders of magnitude faster than a previous solution extended to handle time-dependency.

Indoor Positioning Systems (IPS) have recently received considerable attention, mainly because GPS is unavailable in indoor spaces and consumes considerable energy. On the other hand, predominant Smartphone OS localization subsystems currently rely on server-side localization processes, allowing the service provider to know the location of a user at all times. In this paper, we propose an innovative algorithm for protecting users from location tracking by the localization service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our proposed Temporal Vector Map (TVM) algorithm, allows a user to accurately localize by exploiting a k-Anonymity Bloom (kAB) filter and a bestNeighbors generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. We have evaluated our framework using a real prototype developed in Android and Hadoop HBase as well as realistic Wi-Fi traces scaling-up to several GBs. Our analytical evaluation and experimental study reveal that TVM is not vulnerable to attacks that traditionally compromise k-anonymity protection and indicate that TVM can offer fine-grained localization in approximately four orders of magnitude less energy and number of messages than competitive approaches.

In traffic research, management, and planning a number of path-based analyses are heavily used, e.g., for computing turn-times, evaluating green waves, or studying traffic flow. These analyses require retrieving the trajectories that follow the full path being analyzed. Existing path queries cannot sufficiently support such path-based analyses because they retrieve all trajectories that touch any edge in the path. In this paper, we define and formalize the strict path query. This is a novel query type tailored to support path-based analysis, where trajectories must follow all edges in the path. To efficiently support strict path queries, we present a novel NET work-constrained TRAjectory index (NETTRA). This index enables very efficient retrieval of trajectories that follow a specific path, i.e., strict path queries. NETTRA uses a new path encoding scheme that can determine if a trajectory follows a specific path by only retrieving data from the first and last edge in the path. To correctly answer strict path queries existing network-constrained trajectory indexes must retrieve data from all edges in the path. An extensive performance study of NETTRA using a very large real-world trajectory data set, consisting of 1.7 million trajectories (941 million GPS records) and a road network with 1.3 million edges, shows a speed-up of two orders of magnitude compared to state-of-the-art trajectory indexes.

We present the INTRA system for interactive path-based traffic analysis. The analyses are developed in collaboration with traffic researchers and provide novel insights into conditions such as congestion, travel-time, choice of route, and traffic-flow. INTRA supports interactive point-and-click analysis, due to a novel and efficient indexing structure. With the web-site daisy.aau.dk/its/spqdemo/we will demonstrate several analyses, using a very large real-world data set consisting of 1.9 billion GPS records (1.5 million trajectories) recorded from more than 13 000 vehicles, and touching most of the road network in Denmark.

Mobility data sources feed larger and larger trajectory databases nowadays. Due to the need of extracting useful knowledge patterns that improve services based on users' and customers' behavior, querying and mining such databases has gained significant attention in recent years. However, publishing mobility data may lead to severe privacy violations. In this paper, we present Private-HERMES, an integrated platform for applying data mining and privacy-preserving querying over mobility data. The presented platform provides a two-dimension benchmark framework that includes: (i) a query engine that provides privacy-aware data management functionality of the in-house data via a set of auditing mechanisms that protect the sensitive information against several types of attacks, and (ii) a progressive analysis framework, which, apart from anonymization methods for data publishing, includes various well-known mobility data mining techniques to evaluate the effect of anonymization in the querying and mining results. The demonstration of Private-HERMES via a real-world case study, illustrates the flexibility and usefulness of the platform for supporting privacy-aware data analysis, as well as for providing an extensible blueprint benchmark architecture for privacy-preservation related methods in mobility data.

Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub)trajectories in the MOD. In order to find the most representative subtrajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative subtrajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

Existing approaches for privacy-aware mobility data sharing aim at publishing an anonymized version of the mobility dataset, operating under the assumption that most of the information in the original dataset can be disclosed without causing any privacy violations. In this paper, we assume that the majority of the information that exists in the mobility dataset must remain private and the data has to stay in-house to the hosting organization. To facilitate privacy-aware sharing of the mobility data we develop a trajectory query engine that allows subscribed users to gain restricted access to the database to accomplish various analysis tasks. The proposed engine (i) audits queries for trajectory data to block potential attacks to user privacy, (ii) supports range, distance, and k-nearest neighbors spatial and spatiotemporal queries, and (iii) preserves user anonymity in answers to queries by (a) augmenting the real trajectories with a set of carefully crafted, realistic fake trajectories, and (b) ensuring that no user-specific sensitive locations are reported as part of the returned trajectories.

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.

Mining trajectory databases (TD) has gained great interest due to the popularity of tracking devices. On the other hand, the inherent presence of uncertainty in TD (e.g., due to GPS errors) has not been taken yet into account during the mining process. In this paper, we study the effect of uncertainty in TD clustering and introduce a three-step approach to deal with it. First, we propose an intuitionistic point vector representation of trajectories that encompasses the underlying uncertainty and introduce an effective distance metric to cope with uncertainty. Second, we devise CenTra, a novel algorithm which tackles the problem of discovering the centroid trajectory of a group of movements. Third, we propose a variant of the fuzzy C-means (FCM) clustering algorithm, which embodies CenTra at its update procedure. The experimental evaluation over real world TD demonstrates the efficiency and effectiveness of our approach.

In this work we propose DAEDALUS, a formal framework and system, specifically focussed on progressive combination of mining and querying operators. The core component of DAEDALUS is the MO-DMQL query language that extends SQL in two respects, namely a pattern definition operator and the capability to uniform manipulating both raw data and unveiled patterns. DAEDALUS system is specifically focussed on movement data and has been implemented as a query execution layer on top of the Hermes Moving Object Database. The expressiveness and usefulness of the MODMQL language as well as the computational capabilities of DAEDALUS are qualitatively evaluated by means of a case study.

We present HERMES, a prototype system based on a powerful query language for trajectory databases, which enables the support of aggregative Location-Based Services (LBS). The key observation that motivates HERMES is that the more the knowledge in hand about the trajectory of a mobile user, the better the exploitation of the advances in spatio-temporal query processing for providing intelligent LBS. HERMES is fully incorporated into a state-of-the-art Object-Relational DBMS, and its demonstration illustrates its flexibility and usefulness for delivering custom-defined LBS.