The swift advancement of urbanization has resulted in the growth of numerous large cities, which have enhanced the lives of many individuals but have also created significant challenges, such as air pollution, higher energy consumption, and traffic congestion. Addressing these issues was nearly unfeasible in the past due to the intricate and ever-changing nature of urban environments. Today, however, advancements in sensing technologies and extensive computing infrastructures have generated vast amounts of big data related to urban areas, including information on human mobility, air quality, traffic patterns, and geographic data. Inspired by the potential for creating smarter cities, we developed a vision for urban computing that seeks to harness insights from diverse and extensive data collected in urban settings, using this valuable information to tackle the critical problems our cities currently encounter.

Urbanization's rapid progress has led to many big cities, which have modernized many people's lives but also engendered big challenges, such as air pollution, increased energy consumption, and traffic congestion. Tackling these challenges was nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today.

Urbanization's rapid progress has led to many big cities, which have modernized many people's lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges were nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today.

we present a system that enables testing the impacts of collaborative monitoring of geographical regions by a fleet of drones. Specifically, we consider the settings in which a simulation is executed, based on the properties of a particular approach, and we enable users to gather the basic statistics and compare the parameters of interest for different approaches under varying conditions, as well as observe a basic visualization of the flight paths. In particular, we can vary the number of drones in the fleet, their initial distribution, the occurrences of events of interests (e.g., a potential threat), and the transition of the drones (i.e., their trajectories) in response to a detection of an event. In addition to viewing and analyzing different values of interest, users can upload their collaboration algorithm, add/change the environmental factors, observe the discrepancies, and decide which algorithm is best for their application. Moreover, an authenticated user can save the algorithm and resulting metrics for subsequent retrieval. Our prototype is a web-based system using SpringBoot, a Java framework, relying on MySQL for data management and APIs to communicate with the front-end built on React, enabling various extensibilities (algorithms, environmental parameters, events, drones configuration).

Urbanization's rapid progress has led to many big cities, which have modernized many people's lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges were nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today. This is the eleventh time that we organize this workshop. The previous 10 workshops were hosted with SIGKDD and SIGSPATIAL, each of which attracted over 70 participants and 30 submissions on average.

We consider surveillance of a geographic region by a collaborative system of drones. The drones assist each other in identifying and managing activities of interest on the ground. We also consider an adversary who can create both genuine and fake activities on the ground. The objective of the adversary is to use fake activities, in order to maximize the response time to genuine activities. We present two collaboration algorithms and analyze their response times, as well as the adversary's efforts in terms of the number of fake activities required to achieve a certain response time.

The human brain is probably the most complex object in the universe, and also one of the least understood. For example, how the brain produces the mind and consciousness is a complete mystery. Nevertheless, the brain is amenable to measurements of various kinds that produce lots of data. It is a spatial object residing in the skull; it is also temporal in the sense that neurons communicate by signals that take traverse the brain network over time. In this paper we ask whether spatio-temporal data analysis can contribute to its understanding. Toward this goal we propose several research directions that are inspired by GIS work. However, these are just examples, and other work on moving objects in space or on networks is applicable.

Ride sourcing services such as Uber and Lyft have become widespread in large cities for everyday mobility. When matching passengers, these services attempt to optimize cost savings at a global level. However, a possible scenario is that a passenger A is matched to passenger B even though if A were matched to passenger C, then both A and C would have saved more money. This introduces the concept of "fairness" in ride sharing, which consists of finding the Nash equilibrium in ridesharing. In this paper we compare optimum and fair ridesharing theoretically and experimentally. We show that although theoretically the gap between fair and optimum is large, in practice it is very small.

As a spatio-temporal data-management problem, taxi ridesharing has received a lot of attention recently in the database literature. The broader scientific community, and the commercial world have also addressed the issue through services such as UberPool and Lyftline. The issues addressed have been efficient matching of passengers and taxis, fares, and savings from ridesharing. However, ridesharing fairness has not been addressed so far. Ridesharing fairness is a new problem that we formally define in this paper. We also propose a method of combining the benefits of fair and optimal ridesharing, and of efficiently executing fair and optimal ridesharing queries.

In this short paper, we report on an approach to datamine the brain from a novel perspective, namely traffic analysis. Our data mining approach considers the brain regions and the tracts that connect them as a road network, and the signals traveling between them as vehicles. We analyze travel patterns by a process called traffic assignment. The results are unexpected in the sense that the movement of signals in the brain seems to follow some global optimization patterns as opposed to the anarchical system that would be favored by evolution.

Since in many cities transport infrastructure is operating at or beyond capacity, novel approaches to organize urban mobility are gaining attraction. However, assessing the benefits of a measure that has disruptive capacity in a complex system requires a carefully designed research. This paper takes a recent idea for urban mobility - flexible road trains - and illustrates the computational and research challenges of realizing its full potential and describing its social, ecological and economical impact.

In this paper, we address the problem of finding a resource in a probabilistic setting. In this problem, there are spatially located static resources and a mobile agent. The agent looks to obtain one of the resources while minimizing the cost. This cost may consist of several sub-costs the agent has to pay, from travel time to monetary cost of using a resource. We assume that the agent has no knowledge of exact availability of the resources in real-time, but some prior or partial data gives estimations of this information. This model applies to many situations that arise in urban transportation systems, such as drivers looking for street parking, or taxis looking for new customers. Our approach to the resource search problem only employs uncertain information about resource availability, minimizes the expected cost, and utilizes concepts from decision theory.

Getting a taxi in highly congested areas (e.g. Airports, conferences) is both time consuming and expensive. Chicago Tribune reports that wait at Chicago O'Hare International airport for taxi cabs can be as long as 45 minutes [1]. In this paper we propose RSVP, a ridesharing system that uses walking and virtual pools. RSVP is aimed mainly for transportation hubs, such as airports, railway stations, etc. In these places, a steady stream of passengers arrive via some public transport mode, say train, and then depart to different destinations. We introduce a model for ride-sharing that involves walking, devise ridesharing algorithms, and evaluate them using a database that recorded real taxi trips in NYC.

We consider a model in which there are spatially located static resources on a road network, and a mobile agent. The agent looks to obtain one of the resources, and has no knowledge of exact availability of the resources; only probabilistic information is available. We develop a novel and efficient algorithm that guides the agent through a road network in order to find the desired resource at minimal cost.

We proposed and developed a taxi-sharing system that accepts taxi passengers' real-time ride requests sent from smart phones and schedules proper taxis to pick up them via ride sharing, subject to time, capacity, and monetary constraints. The monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ride sharing and get compensated if their travel time is lengthened due to ride sharing; taxi drivers will make money for all the detour distance due to ride sharing. While such a system is of significant social and environmental benefit, e.g., saving energy consumption and satisfying people's commute, real-time taxi-sharing has not been well studied yet. To this end, we devise a mobile-cloud architecture based taxi-sharing system. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by a spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance. We built an experimental platform using the GPS trajectories generated by over 33,000 taxis over a period of three months. A ride request generator is developed (available at http://cs.uic.edu/~sma/ridesharing) in terms of the stochastic process modelling real ride requests learned from the data set. Tested on this platform with extensive experiments, our proposed system demonstrated its efficiency, effectiveness and scalability. For example, when the ratio of the number of ride requests to the number of taxis is 6, our proposed system serves three times as many taxi riders as that when no ridesharing is performed while saving 11 percent in total travel distance and 7 percent taxi fare per rider.

We propose the E-VeT system for efficient vehicular traffic management in road networks using economy-based reward/penalty schemes. In E-VeT, base stations collaboratively facilitate dynamic vehicular route assignments for reducing the traffic congestion, average time of arrival and fuel consumption. The main contributions of this work are two-fold. First, it proposes an R2A (Revenue-based Route Allocation) scheme, which rewards vehicles for following system-assigned longer-time paths, and charges a fee for following system-assigned shorter-time paths. Furthermore, it penalizes vehicles for any deviations from the system-assigned paths. Second, it discusses a route allocation algorithm, which gives lesser-time paths as a preference to vehicles that have earned higher revenue based on the R2A scheme. Preliminary performance study shows that E-VeT is indeed effective in managing vehicular traffic in road networks by reducing the average time of arrival and fuel consumption.

No abstract available.

Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

Ridesharing is a promising method to address transportation problems such as traffic jams and parking. Although traditional carpooling and taxi ridesharing have been investigated by many, slugging, as a simple yet effective form of ridesharing, has not been well-studied. In this paper, we formally define the slugging problem and its generalization. We provide proofs of their computational time complexity. For the variants of the slugging problem that are constrained by the vehicle capacity and travel time delay, we prove NP-completeness and also propose some effective heuristics. In addition, we discuss the dynamic slugging problem. We conducted experiments using a GPS trajectory data set containing 60 thousand trips. The experimental results show that our proposed heuristics can achieve close-to-optimal performances, which means as much as 59% saving in vehicle travel distance.

Taxi ridesharing can be of significant social and environmental benefit, e.g. by saving energy consumption and satisfying people's commute needs. Despite the great potential, taxi ridesharing, especially with dynamic queries, is not well studied. In this paper, we formally define the dynamic ridesharing problem and propose a large-scale taxi ridesharing service. It efficiently serves real-time requests sent by taxi users and generates ridesharing schedules that reduce the total travel distance significantly. In our method, we first propose a taxi searching algorithm using a spatio-temporal index to quickly retrieve candidate taxis that are likely to satisfy a user query. A scheduling algorithm is then proposed. It checks each candidate taxi and inserts the query's trip into the schedule of the taxi which satisfies the query with minimum additional incurred travel distance. To tackle the heavy computational load, a lazy shortest path calculation strategy is devised to speed up the scheduling algorithm. We evaluated our service using a GPS trajectory dataset generated by over 33,000 taxis during a period of 3 months. By learning the spatio-temporal distributions of real user queries from this dataset, we built an experimental platform that simulates user real behaviours in taking a taxi. Tested on this platform with extensive experiments, our approach demonstrated its efficiency, effectiveness, and scalability. For example, our proposed service serves 25% additional taxi users while saving 13% travel distance compared with no-ridesharing (when the ratio of the number of queries to that of taxis is 6).

Real-time parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, we present a software solution called PhonePark for detecting the availability of on-street parking spaces. The solution uses the GPS and/or accelerometer sensors in a traveler's mobile phone to automatically detect when and where the traveler parked her car, and when she released a parking slot. PhonePark can also utilize the mobile phone's Bluetooth sensor or piggyback on street parking payment transactions for parking activity detection. Thus, the solution considers only mobile phones and does not rely on any external sensors such as cameras, wireless sensors embedded in the pavements, or ultrasonic sensors on vehicles. Further contributions include an algorithm to compute the historical parking availability profile for an arbitrary street block and algorithms to estimate the parking availability in real-time for a given street block. The algorithms are evaluated using real-time and real world street parking data.

The proliferation of mobile devices, location-based services and embedded wireless sensors has given rise to applications that seek to improve the efficiency of the transportation system. In particular, new applications are already available that help travelers to find parking in urban settings by conveying the parking slot availability near the desired destinations of travelers on their mobile devices.

In this paper we present a model of spatially located mobile agents and static resources, in which the agents are looking to obtain one of the resources while minimizing their costs to obtain the resource. The proliferation of mobile devices, location-based services and embedded wireless sensors has given rise to applications that could help the mobile agents have updated information of the location of the resources they are looking for. Nevertheless, while engaged in driving, travelers are better suited being guided to an ideal resource, rather than looking at a map and deciding which available resource to visit. Then the question of how an application should choose this ideal resource, to guide the agent towards it, becomes relevant. In this work we develop algorithms that are designed to guide users to these resources. They use a gravitational approach to guide a mobile agent through a road network in order to find this ideal resource. The performance of the algorithms is evaluated through simulations.

With the proliferation of location-based services, mobile devices, and embedded wireless sensors, more and more applications are being developed to improve the efficiency of the transportation system. In particular, new applications are arising to help vehicles locate open parking slots. Nevertheless, while engaged in driving, travelers are better suited being guided to an ideal parking slot, than looking at a map and choosing which slot to go to. Then the question of how an application should choose this ideal parking slot becomes relevant. Vehicular parking can be viewed as vehicles (players) competing for parking slots (resources with different costs). Based on this competition, we present a game-theoretic framework to analyze parking situations. We introduce and analyze parking slot assignment games and present algorithms that choose parking slots ideally in competitive parking simulations. We also present algorithms for incomplete information contexts and show how these algorithms outperform even algorithms with complete information in some cases.

Real-time street parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, an advanced street parking system called PhonePark is presented. Using the GPS, accelerometer, and Bluetooth sensors on a traveler's mobile phone, in conjunction with geospatial data, we can automatically detect when and where the traveler parked her car, and when she released a parking slot.

With the proliferation of location-based services, mobile devices, and embedded wireless sensors, more and more applications are being developed to improve the efficiency of the transportation system. In particular, new applications are arising to help vehicles locate open parking spaces. Nevertheless, while engaged in driving, travelers are better suited being guided to a particular and ideal parking slot, than looking at a map and choosing which spot to go to. Then the question of how an application should choose this ideal parking spot becomes relevant.

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.

We address the problem of efficient processing of spatio-temporal range queries for moving objects whose whereabouts in time are not known exactly. The fundamental question tackled by such queries is, given a spatial region and a temporal interval, retrieve the objects that were inside the region during the given interval. As earlier works have demonstrated, when the location, time information is uncertain, syntactic constructs are needed to capture the impact of the uncertainty, along with the corresponding processing algorithms. In this work, we focus on the uncertainty model that represents the whereabouts in-between two known locations as a bead and an uncertain trajectory is represented as a necklace -- a sequence of beads. For each syntactic variant of the range query, we present the respective processing algorithms and, in addition, we propose pruning strategies that speed up the generation of the queries' answers. We also present the experimental observations that quantify the benefits of our proposed methodologies.

This paper introduces the problem of modeling urban transportation systems in a database where certain aspects of the data are probabilistic in nature. The transportation network is composed of multiple modes (e.g., automobile, bus, train, pedestrian) that the user can alternate between. A trip – a path between an origin and destination subject to some constraints – is the central concept. How these trips and the network can be represented as both a graph and relational model, as well as the requirements for querying are the main contributions of this paper. A set of operators are defined to work over these transportation concepts and they are integrated within a SQL-like syntax to express queries over the uncertain transportation network. Additionally, the paper shows how this model can be integrated within other moving objects and spatio-temporal data models, and how these graph-based queries can be processed. 1.

Networks of sensors are used in many different fields, from industrial applications to surveillance applications. A common feature of these applications is the necessity of a monitoring infrastructure that analyzes a large number of data streams and outputs values that satisfy certain constraints.

In this paper we consider a novel multimedia application, in which drivers may query multimedia clips captured by smartphones mounted on other vehicles. These multimedia clips visualize and voice-indicate the real-time traffic conditions on road segments ahead. We designed a systematic and exhaustive set of query processing strategies which differ from each other in terms of push versus pull, whether infrastructure communication is utilized, and whether metadata dissemination is separated from multimedia clip dissemination. We analyze these strategies theoretically and by simulations, and identify the one that is superior to the others.

The in-network query processing paradigm in sensor networks postulates that a query is routed among sensors and collects the answers from the sensors on its trajectory. It works for static and connected sensor networks. However, when the network consists of mobile sensors and is sparse, a different approach is necessary. In this paper we propose a query processing method that uses cooperative caching. It makes the data items satisfying a query flow to its originator. To cope with communication bandwidth and storage constraints, the method prioritizes the data-items in terms of their value, as reflected by supply and demand. Simulations based on real-life mobility traces identify the situations in which our approach outperforms a series of existing cooperative caching strategies and an existing mobile sensor network algorithm.

In this paper we study in-network query processing in disconnected mobile environments, where both ad-hoc communication and infrastructure communication are available. Depending on how the infrastructure is utilized, various query-processing schemes are classified. Analytical models are developed to compute the average delay and the average energy consumption for each of these schemes. Based on the analytical models, the query processing schemes are compared under various environment conditions. It is found that none of the studied schemes is optimal in all the conditions. Therefore the paper provides a method that allows the optimal scheme to be chosen for a given set of environmental conditions.

In this paper, we consider a traffic information sharing system based on Floating Car Data (FCD). FCD is one of the methods used to gather traffic information; it uses vehicles as sensor nodes that transmit their speed to the server. The traffic information sharing system broadcasts speed information updated by such transmission. Vehicles receiving broadcasted speed information can calculate travel time and select a minimum time route. The communication cost and the load of the server are issues, because such a traffic information sharing system can generate a lot of wireless communication between the vehicles and the server [2][3]. However, reducing the amount of communication lowers the accuracy of information provided by the server. In this paper we propose an Information Cost Model to quantify a trade-off relationship between the communication cost of the system and the accuracy of information. Additionally, we propose a randomized method to reduce the number of messages from clients to the server by avoiding redundant transmissions. We compared the performance of our proposed method with that of a conventional method, using real traffic data from Chicago highways. The result shows that our proposed method generally outperforms the conventional method.

Peer-to-peer data dissemination in a mobile ad-hoc environment is characterized by three resource constraints, including energy, communication bandwidth, and storage. Most of the existing studies deal with these constraints separately. In this paper we propose an algorithm called RANk-based dissemination (RANDI), which provides an integral treatment to the three constraints. The contribution is in determining how to prioritize the reports in terms of their relevance, when to transmit the reports, and how many to transmit. We experimentally compare RANDI with IDS and PeopleNet, two mobile peer-to-peer dissemination algorithms. The results show that RANDI significantly outperforms both algorithms.

A mobile ad-hoc network (MANET) is a set of moving objects that communicate with each other via unregulated, short-range wireless technologies such as IEEE 802.11, Bluetooth, or Ultra Wide Band (UWB). No fixed infrastructure is assumed or relied upon. An important application domain of MANET’s is local resource discovery. In a local resource discovery application, a user finds local resources that satisfy specified criteria. For example, a driver finds an available parking slot in a region by receiving information generated by the parking meter, or gets the traffic conditions on a highway segment a mile ahead; a cab driver finds a near-by customer, or a participant at a convention finds another participant with a matching profile.

With help of context, computer systems and applications could be more user-friendly, flexible and adaptable. With semantic locations, applications can understand users better or provide helpful services. We propose a method that automatically derives semantic locations from user’s trace. Our experimental results show that the proposed method identities up to 96% correct semantic locations.

I will describe a project that establishes a graduate training program in the Information Technology aspects of Transportation Science. Computational transportation scientists will develop the next generation of intelligent transportation systems, aimed at addressing inefficiencies that cause excessive environmental pollution, fuel consumption, risk to public safety, and congestion. The trainees will investigate technologies in which sensors, traveler-devices such as PDA’s, in-vehicle computers, and computers in the static infrastructure are integrated into a collaborative environment. Trainees will also investigate both how these technologies are adopted and their impacts. The envisioned transportation environment will incorporate the technologies, enabling solutions to problems such as dynamic ridesharing, real-time routing, and navigation. Basic research in information management, communications, software architectures, modeling tools, human factors, traffic prediction, and transportation planning will be utilized to found a new discipline that will integrate millions of highly mobile computers and sensors into a collaborative system. The trainees will build a prototype test-bed application that will integrate the results of their research. This prototype is a software system that runs on hand-held computers, and plans optimal trajectories for a traveler using multiple modes of transportation (e.g., bus and then train).

In this paper we examine resource discovery in inter-vehicle ad-hoc networks in an urban area, where moving vehicles communicate with each other via short-range wireless transmission. Our focus is on real-time location-specific information. We explore an opportunistic approach to resource recovery, in which a vehicle obtains information about resources from encountered vehicles. The vehicle uses a spatio-temporal relevance function to sort the resources, and save only the most relevant ones. Our theoretical and experimental analysis indicates that the opportunistic exchange algorithm automatically limits the distribution of a resource to a bounded spatial area and to the duration for which the resource is of interest.

This article addresses the problem of managing Moving Objects Databases (MODs) which capture the inherent imprecision of the information about the moving object's location at a given time. We deal systematically with the issues of constructing and representing the trajectories of moving objects and querying the MOD. We propose to model an uncertain trajectory as a three-dimensional (3D) cylindrical body and we introduce a set of novel but natural spatio-temporal operators which capture the uncertainty and are used to express spatio-temporal range queries. We devise and analyze algorithms for processing the operators and demonstrate that the model incorporates the uncertainty in a manner which enables efficient querying, thus striking a balance between the modeling power and computational efficiency. We address some implementation aspects which we experienced in our DOMINO project, as a part of which the operators that we introduce have been implemented. We also report on some experimental observations of a practical relevance.

We consider the problem of placing copies of objects at multiple locations in a distributed system, whose interconnection network is a tree, in order to minimize the cost of servicing read and write requests to the objects. We assume that the tree nodes have limited storage and the number of copies permitted may be limited. The set of nodes that have a copy of the object, called replica nodes, constitute the replica set of the object. Read requests of a node are serviced from the closest replica node. Write requests of a node are propagated to all the replicas of the object using a minimum cost Steiner tree that includes the writer and all replica nodes. The total cost associated with a replica set equals the cost of servicing all the read and write requests, plus the storage cost at all the replica nodes. We are interested in finding a replica set with minimum total cost, i.e. a Steiner-optimal replica set. Given a tree with n nodes, we provide an O(n/sup 6/p/sup 2/)-time algorithm for finding a Steiner-optimal replica set of size p, taking into consideration the read, write, and storage costs. Our algorithm can also find a Steiner-optimal replica set for a tree with n nodes in time O(n/sup 8/). We also demonstrate that the policy used to propagate write requests to all the replica nodes in the network affects the cost and configuration of the optimal replica set for the object.

Our Mobitrack prototype is intended to serve as a platform, or a toolkit for developing moving-objects-database type of applications. The system is the third in a three-layer architecture. The first layer is an object relational DBMS. The database stores the information about each moving object, including its plan of motion. The second layer is a GIS that adds capabilities and user interface primitives for storing, querying, and manipulating geographic information. The third layer, Mobitrack, adds temporal capabilities, capabilities of managing the uncertainty that is inherent in future motion plans, capabilities for location prediction, and a simulation testbed. Currently, Mobitrack runs on both Unix and MS Windows. On both platforms Mobitrack uses the Arc-View GIS. It uses the Informix DBMS on Unix, and DBAccess on MS Windows.

Consider a database that represents information about moving objects and their location. For example, for a database representing the location of taxi-cabs a typical query may be: retrieve the free cabs that are currently within 1 mile of 33 N. Michigan Ave., Chicago (to pick-up a customer); or for a trucking company database a typical query may be: retrieve the trucks that are currently within 1 mile of truck ABT312 (which needs assistance); or for a database representing the current location of objects in a battlefield a typical query may be: retrieve the friendly helicopters that are in a given region, or, retrieve the friendly helicopters that are expected to enter the region within the next 10 minutes. The queries may originate from the moving objects, or from stationary users. We will refer to applications with the above characteristics as moving-objects-database (MOD) applications, and to queries as the ones mentioned above as MOD queries.

Consider a database that represents the location of moving objects, such as taxi-cabs (typical query: "retrieve the cabs that are currently within 1 mile of 33 Michigan Ave., Chicago"), or objects in a battle-field. Existing database management systems (DBMSs) are not well equipped to handle continuously changing data, such as the position of moving objects, since data is assumed to be constant unless it is explicitly modified. In this paper, we address position-update policies and imprecision. Assuming that the actual position of a moving object m deviates from the position computed by the DBMS, when should m update its position in the database in order to eliminate the deviation? Furthermore, how can the DBMS provide a bound on the error (i.e. the deviation) when it replies to a query, such as: "what is the current position of m?" We propose a cost-based approach to update policies that answers both questions. We develop several update policies and analyze them theoretically and experimentally.

Consider a database that represents information about moving objects and their location. For example, for a database representing the location of taxi-cabs a typical query may be: retrieve the free cabs that are currently within 1 mile of 33 N. Michigan Ave., Chicago (to pickup a customer). In the military, moving object database applications arise in the context of the digital battlefield and in the civilian industry they arise in transportation systems. Currently, moving object database applications are being developed in an ad hoc fashion. Database management system (DBMS) technology provides a potential foundation upon which to develop these applications, however DBMSs are currently not used for this purpose. The reason is that there is a critical set of capabilities that are needed by moving object database applications and are lacking in existing DBMSs. The objective of our Databases fOr MovINg Objects (DOMINO) project is to build an envelope containing these capabilities on top of existing DBMSs. We describe the problems and our proposed solutions.

One of the features that distinguishes digital libraries from traditional databases is new cost models for client access to intellectual property. Clients will pay for accessing data items in digital libraries, and we believe that optimizing these costs will be as important as optimizing performance in traditional databases. In this article we discuss cost models and protocols for accessing digital libraries, with the objective of determining the minimum cost protocol for each model. We expect that in the future information appliances will come equipped with a cost optimizer, in the same way that computers today come with a built-in operating system. This article makes the initial steps towards a thery and practice of intellectual property cost management.

We propose a data model for representing moving objects in database systems. It is called the Moving Objects Spatio-Temporal (MOST) data model. We also propose Future Temporal Logic (FTL) as the query language for the MOST model, and devise an algorithm for processing FTL queries in MOST.

This article addresses the performance of distributed database systems. Specifically, we present an algorithm for dynamic replication of an object in distributed systems. The algorithm is adaptive in the sence that it changes the replication scheme of the object i.e., the set of processors at which the object inreplicated) as changes occur in the read-write patern of the object (i.e., the number of reads and writes issued by each processor). The algorithm continuously moves the replication scheme towards an optimal one. We show that the algorithm can be combined with the concurrency control and recovery mechanisms of ta distributed database management system. The performance of the algorithm is analyzed theoretically and experimentally. On the way we provide a lower bound on the performance of any dynamic replication algorith.

In this paper, we present a unified formalism, based on Past Temporal Logic, for specifying conditions and events in the rules for active database system. This language permits specification of many time varying properties of database systems. It also permits specification of temporal aggregates. We present an efficient incremental algorithm for detecting conditions specified in this language. The given algorithm, for a subclass of the logic, was implemented on top of Sybase. 0 1 Introduction The most popular model of rules in active database systems is the ECA model [41, 6, 28, 5, 14, 18]. It defines a rule to consist of three parts, event, condition, and action. The semantics is that whenever the event happens, the condition (which is usually a database query) is evaluated, and if satisfied then the action is taken. The event may be composite and temporal, such as, transaction A starts after transaction B ended. However, the condition is static in the sense that it refers to the cu...

In this paper we propose two languages, called Future Temporal Logic (FTL) and Past Temporal Logic (PTL), for specifying temporal triggers. Some examples of trigger conditions that can be specified in our language are the following: "The value of a certain attribute increases by more than 10% in 10 minutes," "A tuple that satisfies a certain predicate is added to the database at least 10 minutes before another tuple, satisfying a different condition, is added to the database." Such triggers are important for monitor and control applications. In addition to the languages, we present algorithms for processing the trigger conditions specified in these languages, namely, procedures for determining when the trigger conditions are satisfied. These methods can be added as a "temporal" component to an existing database management systems. A preliminary prototype of the temporal component that uses the FTL language has been built on top of Sybase running on SUN workstations.>

This paper makes two contributions. First, we introduce a model for evaluating the performance of data allocation and replication algorithms in distributed databases. The model is comprehensive in the sense that it accounts for I/O cost, for communication cost, and for limits on the minimum number of copies of the object (to ensure availability). The second contribution of this paper is the introduction and analysis of an algorithm for automatic dynamic allocation of replicas to processors. Using the new model, we compare the performance of the traditional read-one-write-all static allocation algorithm, to the performance of the dynamic allocation algorithm. As a result, we obtain the relationship between the communication cost and I/O cost for which static allocation is superior to dynamic allocation, and the relationships for which dynamic allocation is superior.>

Users of mobile computers will soon have online access to a large number of databases via wireless networks. Because of limited bandwidth, wireless communication is more expensive than wire communication. In this paper we present and analyze various static and dynamic data allocation methods. The objective is to optimize the communication cost between a mobile computer and the stationary computer that stores the online database. Analysis is performed in two cost models. One is connection (or time) based, as in cellular telephones, where the user is charged per minute of connection. The other is message based, as in packet radio networks, where the user is charged per message. Our analysis addresses both, the average case and the worst case for determining the best allocation method.

A distributed algorithm for dynamic data replication of an object in a distributed system is presented. The algorithm changes the number of replicas and their location in the distributed system to optimize the amount of communication. The algorithm dynamically adapts the replication scheme of an object to the pattern of read-write requests in the distributed system. It is shown that the cost of the algorithm is within a constant factor of the lower bound.>

The parallel evaluation of datalog rule programs, mainly by processors that are interconnected by a communication network, is discussed. Data-reduction, a paradigm for the parallel evaluation of a datalog program, is introduced. Parallelization is accomplished by partitioning the rule-instantiations among the processors. After presenting the paradigm, its implementation with seminaive evaluation, its communication overhead, and its application to stratified-negation datalog programs are discussed. It is proven that decomposability, a related concept introduced in previous works, is undecidable.>

We present two distributed algorithms for dynamic replication of a data-item in communication networks. The algorithms are adaptive in the sense that they change the replication scheme of the item (i.e. the set of processors at which the data-item is replicated), as the read-write pattern of the processors in the network changes. Each algorithm continuously moves the replication scheme towards an optimal one, where optimality is defined with respect to different objective functions. One algorithm optimizes the communication cost objective function, and the other optimizes the communication time. We also provide a lower bound on the performance of any dynamic replication algorithm.

Rule interpreters usually start with an initial database and perform the inference procedure in cycles, ending with a final database. In a real time environment it is possible to receive updates to the initial database after the inference procedure has started or even after it has ended. We present an algorithm for incremental maintenance of the deductive database in the presence of such updates. Interestingly, the same algorithm is useful for parallel and distributed rule processing in the following sense. \\'hen the processors evaluating a program operate asynchronously. then they may have different views of the database. The incremental maintenance procedure we present can be used to synchronize these views.

In this paper we consider the communication complexity of maintaining the replicas of a logical data-item, in a database distributed over a computer network. We propose a new method, called the minimum spanning tree write, by which a processor in the network should multicast a write of a logical data-item, to all the processors that store replicas of the items. Then we show that the minimum spanning tree write is optimal from the communication cost point of view. We also demonstate that the method by which a write is multicast to all the replicas of a data-item affects the optimal replication scheme of the item, i.e., at which processors in the network the replicas should be located. Therefore, next we consider the problem of determining an optimal replicaiton scheme for a data item, assuming that each processor employs the minimum spanning tree write at run-time. The problem for general networks is shown NP-Complete, but we provide efficient algorithms to obtain an optimal allocation scheme for three common types of network topologies. They are completely-connected, tree, and ring networks. For these topologies, efficient algorithms are also provided for the case in which reliability considerations dictate a minimum number of replicas.

This paper is concerned with the parallel evaluation of datalog rule programs, mainly by processors that are interconnected by a communication network. We introduce a paradigm, called data-reduction, for the parallel evaluation of a general datalog program. Several parallelization strategies discussed previously in [CW, GST, W, WS] are special cases of this paradigm. The paradigm parallelizes the evaluation by partitioning among the processors the instantiations of the rules. After presenting the paradigm, we discuss the following issues, that we see fundamental for parallelization strategies derived from the paradigm properties of the strategies that enable a reduction in the communication overhead, decomposability, load balancing, and application to programs with negation. We prove that decomposability, a concept introduced previously in [WS, CW], is undecidable.

We argue that the appropriate parallelization strategy for logic-program evaluation depends on the program being evaluated. Therefore, this paper is concerned with the issues of program-classification, and parallelization-strategies. We propose five parallelization strategies that differ based on the following criteria. Their evaluation cost, the overhead of communication and synchronization among processors, and the programs to which they are applicable. In particular, we start our study with pure-parallelization, i.e., parallelization without overhead. An interesting class-structure of logic programs is demonstrated, when considering amenability to pure-parallelization. The relationship to the NC complexity class is discussed. Then we propose strategies that do incur an overhead, but are optimal in a sense that will be precisely defined.

The authors consider distributed algorithms that achieve transaction commitment at minimal communication cost but differ in the time it takes them to reach consensus. Based on this criterion, the authors define a 'better than' partial ranking of minimal-communication-cost algorithms. They also discuss alternatives of a simple, minimal-communication-cost algorithm introduced previously, called TREE-COMMIT.>

This paper is concerned with the issue of parallel evaluation of logic programs. To address this issue we define a new concept of predicate decomposability. If a predicate is decomposable, it means that the load of evaluating it can be divided among a number of processors, without a need for communication among them. This in turn results in a very significant speed-up of the evaluation process.

The main purpose of a locking protocol is to ensure correct interleaving of actions executed by concurrent transactions. The locking protocol consists of a set of rules dictating how accessed entities should be locked and unlocked. As a result of obeying the rules, transactions incur an overhead, particularly in a distributed database. We propose three measures for evaluating this overhead, each most suitable to a different type of underlying communication network. Then, using a graph theoretic model, we analyze and compare three protocols according to each measure: two-phase-locking ([EGLT]), two-phase-locking with a fixed order imposed on the database-entities (ensuring deadlock freedom), and the tree protocol ([SK]). The combined overhead of the locking protocol and the two phase commit protocol ([G]) is also determined.

We consider the communication protocol for transaction commitment in a distributed database. Specifically, the connection between the structure of communication among the participating sites, and the communication network topology is investigated. In order to do so, the cost of transaction commitment is defined as the number of network hops that messages of the protocol must traverse. We establish the necessary cost for transaction commitment, and show that it is also sufficient. A simple distributed algorithm is presented to prove sufficiency. Our algorithm is also time-efficient, and in order to prove that we show that the timing of our algorithm is optimal within a natural class of commit-protocols.

The main purpose of a locking protocol is to ensure correct interleaving of actions executed by concurrent transactions. The locking protocol consists of a set of rules dictating how accessed entities should be locked and unlocked. As a result of obeying the rules, transactions in a distributed database incur an overhead. We propose three measures of evaluating this overhead, each most suitable to a different type of underlying communication network. Then, using a graph theoretic model, we analyze and compare three protocols according to each measure: two-phase locking, two-phase locking with a fixed order imposed on the database entities (ensuring deadlock freedom), and the tree protocol. In practice, a transaction also executes the two-phase commit protocol in order to guarantee atomicity. Therefore, the combined overhead of each locking protocol and the two-phase commit protocol is also determined.

Article Free Access Share on Deadlock-freedom (and saftey) of transactions in a distributed database Authors: Ouri Wolfson Technica, Israel Institute of Technology, Computer Science Dept., Haifa 32000, Israel and AT&T Bell Laboratories, Short Hills, New Jersey Technica, Israel Institute of Technology, Computer Science Dept., Haifa 32000, Israel and AT&T Bell Laboratories, Short Hills, New JerseyView Profile , Mihalis Yannakakis AT&T Bell Laboratories, Murray Hill, New Jersey AT&T Bell Laboratories, Murray Hill, New JerseyView Profile Authors Info & Claims PODS '85: Proceedings of the fourth ACM SIGACT-SIGMOD symposium on Principles of database systemsMarch 1985 Pages 105–112https://doi.org/10.1145/325405.325418Published:25 March 1985Publication History 3citation321DownloadsMetricsTotal Citations3Total Downloads321Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In Distributed Databases the typical problems of Centralized Databases become more difficult. One of them is Concurrency Control. It can be summarized as follows. Users of the Database access it by executing transactions. Different transactions are executed concurrently therefore their actions interleave. Without proper control this interleaving may produce incorrect results, even if individual transactions are correct. The Concurrency Control process has to prevent these situations. There are several possible mechanisms for controlling concurrency, of which the most widely used is Locking. In this paper we examine and analyze Locking as a Concurrency Control mechanism for Distributed Databases. We define Distributed Locking Policies (methods for locking entities in Distributed Databases) and show how existing Policies for a Centralized Database generalize to the Distributed case. We also define a new category of Distributed Locking Policies, D-policies, into which these generalizations fall. An algorithm which determines whether all transactions of a given D-policy are guaranteed to produce only correct interleavings (are safe) is presented. The algorithm is efficient, even though testing an arbitrary set of transactions for safety is coNP-complete. However, we prove that optimal locking of transactions to satisfy the conditions tested by the algorithm is NP-hard even for a Centralized Database.