Optimal Time-dependent Sequenced Route Queries in Road Networks

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. For instance, considering a city road network at a given departure time, one can find the fastest route between one's work and his/her home, passing through a bank, a supermarket and a restaurant, in this order. The main contribution of our work is the consideration of the time dependency of the network, a realistic characteristic of urban road networks, which has not been considered previously when addressing the optimal sequenced route query. Our approach uses the A* search paradigm that is equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. In order to compare our proposal we extended a previously proposed solution aimed at non-time dependent sequenced route queries, enabling it to deal with the time-dependency. Our experiments using real and synthetic data sets have shown our proposed solution to be up to two orders of magnitude faster than the temporally extended previous solution.Comment: 10 pages, 12 figures To be published as a short paper in the 23rd ACM SIGSPATIA

Alternative group trip planning queries in spatial databases

Trip Planning Queries are considered as one of the popular services offered by Location-Based Services. We propose a new query type called an Alternative Group Trip Planning Query (AGTPQ) which is an extended version of Sequenced Group Trip Planning Queries (SGTPQs). Given a set of users’ source locations and destination locations and a sequence of Categories of Interest (COIs) that the users want to visit, an AGTPQ generates a new COI sequence order using one of the proposed techniques and finds an optimal trip starting from the source locations, passing through the new sequenced COI order and ending at the destination locations. We propose three approaches: Permutation Strategy on Sequenced Group Trip Planning Queries (PSGTPQs), Greedy Strategy on Sequenced Group Trip Planning Queries (GSGTPQs) and Random Strategy on Sequenced Group Trip Planning Queries (RSGTPQs). We compare the results of our proposed strategies with the PGNE strategy through experimental evaluation

Dynamic group trip planning queries in spatial databases

Trip planning queries are considered an integral part of Location Based Services. The advancement of positioning devices and highly available internet facilities enable users to access network information from anywhere at any time. In our research, we investigated Sequential Group Trip Planning (SGTP) queries. Given a set of starting and destination locations and an ordered sequence of Categories of Interests (COIs) for a group of users, a SGTP query returns the route for each user from their respective start and destination locations that minimizes the overall travel distance. We propose two approaches: Dynamic Group Trip Planning (DGTP) and Modified Dynamic Group Trip Planning (M-DGTP). The proposed DGTP approach enables users to plan a group trip in a more flexible manner and the M-DGTP approach optimizes the total travel distance of the group. We compare the results of our proposed strategies with an existing strategy called N-DGTP through experimental evaluation.School of Graduate Studies (SGS) of the University of Lethbridg

Continuous Obstructed Detour Queries

In this paper, we introduce Continuous Obstructed Detour (COD) Queries, a novel query type in spatial databases. COD queries continuously return the nearest point of interests (POIs) such as a restaurant, an ATM machine and a pharmacy with respect to the current location and the fixed destination of a moving pedestrian in presence of obstacles like a fence, a lake or a private building. The path towards a destination is typically not predetermined and the nearest POIs can change over time with the change of a pedestrian\u27s current location towards a fixed destination. The distance to a POI is measured as the summation of the obstructed distance from the pedestrian\u27s current location to the POI and the obstructed distance from the POI to the pedestrian\u27s destination. Evaluating the query for every change of a pedestrian\u27s location would incur extremely high processing overhead. We develop an efficient solution for COD queries and verify the effectiveness and efficiency of our solution in experiments

Searching and mining in enriched geo-spatial data

The emergence of new data collection mechanisms in geo-spatial applications paired with a heightened tendency of users to volunteer information provides an ever-increasing flow of data of high volume, complex nature, and often associated with inherent uncertainty. Such mechanisms include crowdsourcing, automated knowledge inference, tracking, and social media data repositories. Such data bearing additional information from multiple sources like probability distributions, text or numerical attributes, social context, or multimedia content can be called multi-enriched. Searching and mining this abundance of information holds many challenges, if all of the data's potential is to be released. This thesis addresses several major issues arising in that field, namely path queries using multi-enriched data, trend mining in social media data, and handling uncertainty in geo-spatial data. In all cases, the developed methods have made significant contributions and have appeared in or were accepted into various renowned international peer-reviewed venues. A common use of geo-spatial data is path queries in road networks where traditional methods optimise results based on absolute and ofttimes singular metrics, i.e., finding the shortest paths based on distance or the best trade-off between distance and travel time. Integrating additional aspects like qualitative or social data by enriching the data model with knowledge derived from sources as mentioned above allows for queries that can be issued to fit a broader scope of needs or preferences. This thesis presents two implementations of incorporating multi-enriched data into road networks. In one case, a range of qualitative data sources is evaluated to gain knowledge about user preferences which is subsequently matched with locations represented in a road network and integrated into its components. Several methods are presented for highly customisable path queries that incorporate a wide spectrum of data. In a second case, a framework is described for resource distribution with reappearance in road networks to serve one or more clients, resulting in paths that provide maximum gain based on a probabilistic evaluation of available resources. Applications for this include finding parking spots. Social media trends are an emerging research area giving insight in user sentiment and important topics. Such trends consist of bursts of messages concerning a certain topic within a time frame, significantly deviating from the average appearance frequency of the same topic. By investigating the dissemination of such trends in space and time, this thesis presents methods to classify trend archetypes to predict future dissemination of a trend. Processing and querying uncertain data is particularly demanding given the additional knowledge required to yield results with probabilistic guarantees. Since such knowledge is not always available and queries are not easily scaled to larger datasets due to the #P-complete nature of the problem, many existing approaches reduce the data to a deterministic representation of its underlying model to eliminate uncertainty. However, data uncertainty can also provide valuable insight into the nature of the data that cannot be represented in a deterministic manner. This thesis presents techniques for clustering uncertain data as well as query processing, that take the additional information from uncertainty models into account while preserving scalability using a sampling-based approach, while previous approaches could only provide one of the two. The given solutions enable the application of various existing clustering techniques or query types to a framework that manages the uncertainty.Das Erscheinen neuer Methoden zur Datenerhebung in räumlichen Applikationen gepaart mit einer erhöhten Bereitschaft der Nutzer, Daten über sich preiszugeben, generiert einen stetig steigenden Fluss von Daten in großer Menge, komplexer Natur, und oft gepaart mit inhärenter Unsicherheit. Beispiele für solche Mechanismen sind Crowdsourcing, automatisierte Wissensinferenz, Tracking, und Daten aus sozialen Medien. Derartige Daten, angereichert mit mit zusätzlichen Informationen aus verschiedenen Quellen wie Wahrscheinlichkeitsverteilungen, Text- oder numerische Attribute, sozialem Kontext, oder Multimediainhalten, werden als multi-enriched bezeichnet. Suche und Datamining in dieser weiten Datenmenge hält viele Herausforderungen bereit, wenn das gesamte Potenzial der Daten genutzt werden soll. Diese Arbeit geht auf mehrere große Fragestellungen in diesem Feld ein, insbesondere Pfadanfragen in multi-enriched Daten, Trend-mining in Daten aus sozialen Netzwerken, und die Beherrschung von Unsicherheit in räumlichen Daten. In all diesen Fällen haben die entwickelten Methoden signifikante Forschungsbeiträge geleistet und wurden veröffentlicht oder angenommen zu diversen renommierten internationalen, von Experten begutachteten Konferenzen und Journals. Ein gängiges Anwendungsgebiet räumlicher Daten sind Pfadanfragen in Straßennetzwerken, wo traditionelle Methoden die Resultate anhand absoluter und oft auch singulärer Maße optimieren, d.h., der kürzeste Pfad in Bezug auf die Distanz oder der beste Kompromiss zwischen Distanz und Reisezeit. Durch die Integration zusätzlicher Aspekte wie qualitativer Daten oder Daten aus sozialen Netzwerken als Anreicherung des Datenmodells mit aus diesen Quellen abgeleitetem Wissen werden Anfragen möglich, die ein breiteres Spektrum an Anforderungen oder Präferenzen erfüllen. Diese Arbeit präsentiert zwei Ansätze, solche multi-enriched Daten in Straßennetze einzufügen. Zum einen wird eine Reihe qualitativer Datenquellen ausgewertet, um Wissen über Nutzerpräferenzen zu generieren, welches darauf mit Örtlichkeiten im Straßennetz abgeglichen und in das Netz integriert wird. Diverse Methoden werden präsentiert, die stark personalisierbare Pfadanfragen ermöglichen, die ein weites Spektrum an Daten mit einbeziehen. Im zweiten Fall wird ein Framework präsentiert, das eine Ressourcenverteilung im Straßennetzwerk modelliert, bei der einmal verbrauchte Ressourcen erneut auftauchen können. Resultierende Pfade ergeben einen maximalen Ertrag basieren auf einer probabilistischen Evaluation der verfügbaren Ressourcen. Eine Anwendung ist die Suche nach Parkplätzen. Trends in sozialen Medien sind ein entstehendes Forscchungsgebiet, das Einblicke in Benutzerverhalten und wichtige Themen zulässt. Solche Trends bestehen aus großen Mengen an Nachrichten zu einem bestimmten Thema innerhalb eines Zeitfensters, so dass die Auftrittsfrequenz signifikant über den durchschnittlichen Level liegt. Durch die Untersuchung der Fortpflanzung solcher Trends in Raum und Zeit präsentiert diese Arbeit Methoden, um Trends nach Archetypen zu klassifizieren und ihren zukünftigen Weg vorherzusagen. Die Anfragebearbeitung und Datamining in unsicheren Daten ist besonders herausfordernd, insbesondere im Hinblick auf das notwendige Zusatzwissen, um Resultate mit probabilistischen Garantien zu erzielen. Solches Wissen ist nicht immer verfügbar und Anfragen lassen sich aufgrund der \P-Vollständigkeit des Problems nicht ohne Weiteres auf größere Datensätze skalieren. Dennoch kann Datenunsicherheit wertvollen Einblick in die Struktur der Daten liefern, der mit deterministischen Methoden nicht erreichbar wäre. Diese Arbeit präsentiert Techniken zum Clustering unsicherer Daten sowie zur Anfragebearbeitung, die die Zusatzinformation aus dem Unsicherheitsmodell in Betracht ziehen, jedoch gleichzeitig die Skalierbarkeit des Ansatzes auf große Datenmengen sicherstellen

Performance metrics and routing in vehicular ad hoc networks

The aim of this thesis is to propose a method for enhancing the performance of Vehicular Ad hoc Networks (VANETs). The focus is on a routing protocol where performance metrics are used to inform the routing decisions made. The thesis begins by analysing routing protocols in a random mobility scenario with a wide range of node densities. A Cellular Automata algorithm is subsequently applied in order to create a mobility model of a highway, and wide range of density and transmission range are tested. Performance metrics are introduced to assist the prediction of likely route failure. The Good Link Availability (GLA) and Good Route Availability (GRA) metrics are proposed which can be used for a pre-emptive action that has the potential to give better performance. The implementation framework for this method using the AODV routing protocol is also discussed. The main outcomes of this research can be summarised as identifying and formulating methods for pre-emptive actions using a Cellular Automata with NS-2 to simulate VANETs, and the implementation method within the AODV routing protocol

SNGF Selected Node Geographic Forwarding Routing Protocol for VANETs

This thesis presents a protocol for intervehicle communication for use in Vehicular Ad Hoc Networks (VANET). VANET is a natural extension of mobile ad hoc networks (MANET) in which the restrictions related to power and mobility are relaxed. The routing protocols used for MANETs are generally dependent on the state of the network. With changes in the network topology, routing messages are generated so that the states of the routers in the network are updated. In the case of VANETs, in which the level of node mobility is high, message-routing overhead has serious implications for the scalability and throughput of the routing protocol. This thesis introduces criteria that are recommended for use when protocols are designed for VANET applications and presents the Selected Node Geographic Forwarding (SNGF) protocol. The SNGF protocol implements controlled flooding in an efficient manner in order to reduce unnecessary communication overhead. The protocol has a destination discovery mechanism that allows it to initiate correspondence between nodes without reliance on static location services. The protocol avoids formation of clusters by using the concept of selective forwarding, thus providing the advantages of cluster based approaches without actually forming one itself. It effectively deals with blind flooding by introducing a comprehensive retransmission time delay in the nodes. This retransmission delay favors the nodes in the direction of the destination and prevents other nodes from retransmitting the same message. The SNGF protocol does not use routing tables, which require frequent updates in mobile networks, instead it relies on directing the messages to geographic locations which are forwarded by any available intermediary nodes. The protocol also provides techniques for handling network fragmentation which can be a frequent problem in vehicular networks. It is capable of delayed message transmission and multiple route discovery in the case of the non-availability of the shortest path to the destination. To evaluate the performance of the SNGF protocol, an extensive study of mobile networks was conducted using the NS2 simulator. The simulation results demonstrate the reachability of the protocol, its scalability advantages and its total independence from location services. The SNGF protocol allows each participating node to operate independently of other nodes in the network. Nodes in the network are able to communicate with other nodes without ever becoming dependent on intermediary nodes. This feature opens new possibility for individual node based application development in ad hoc networks. The traffic profiling is described as it would be observed by an independent node participating in VANET using the SNGF protocol. The node communicates with other nodes and collects relevant data through the discourse capability of SNGF. The data collected by the node is viewed as a snapshot in time of the traffic conditions down the road based upon which future traffic condition is predicted. Traffic profiling is investigated for different levels of VANET deployment. The simulation results show that the proposed method of traffic profiling in a VANET environment using the SNGF protocol is viable for even lower levels of deployment