Maintaining a Trajectory Data Warehouse Under Schema Changes with the Mobile Agent View Adaptation Technique

The study of tourist movements and their behavior becomes an important issue. This paper presents a tourist trajectory model based on Event Time of Interest, Region of Interest and Place of Interest. This model enables the generation of trajectory data that will be stored in a trajectory data warehouse. This article develops and constructs an ontology modeling framework that acts as the foundational model platform for the creation of a semantic trajectory data warehouse. In addition, the modeling approach describes thematic dimensions that provide a design platform for analysis and knowledge discovery in the trajectory and processing data for moving objects.

When a database is shared by many users, updates to the database schema are almost always prohibited because there is a risk of making existing application programs obsolete when they run against the modified schema. The paper addresses the problem by integrating schema evolution with view facilities. When new requirements necessitate schema updates for a particular user, then the user specifies schema changes to his personal view, rather than to the shared base schema. Our view schema evolution approach then computes a new view schema that reflects the semantics of the desired schema change, and replaces the old view with the new one. We show that our system provides the means for schema change without affecting other views (and thus without affecting existing application programs). The persistent data is shared by different views of the schema, i.e., both old as well as newly developed applications can continue to interoperate. The paper describes a solution approach of realizing the evolution mechanism as a working system, which as its key feature requires the underlying object oriented view system to support capacity augmenting views. We present algorithms that implement the complete set of typical schema evolution operations as view definitions. Lastly, we describe the transparent schema evolution system (TSE) that we have built on top of GemStone, including our solution for supporting capacity augmenting view mechanisms.

The popularization of mobile devices and satellite navigation systems has brought new opportunities and challenges to many location-based services (LBSs), intelligent transport systems (ITSs) and fleet management systems. Those systems store trajectory data in moving object databases and trajectory data warehouses. Trajectory datasets can be used in many data mining tasks, e.g., for mining traffic patterns, frequently visited places, and so on. However, such data mining tasks introduce security and location privacy threats to companies (data owners) and mobile objects that generate trajectories. In this paper, in order to overcome the location privacy threats, we present a technique that blurs trajectories according to user-defined privacy profiles and a data mining algorithm called MCSPP that is capable of mining continuous sequences from blurred trajectories. To test that our approach works correctly, we have implemented an experimental environment. Moreover, the conducted experiments have shown a good performance and scalability of the MCSPP algorithm.

In this paper we describe our novel use of space syntax for the design and development of pervasive systems. Pervasive systems are computer systems that are designed to be “invisible” to users because they are designed to blend in with their environment and become part of the fabric of everyday life. Pervasive systems consist of fixed, mobile and embedded components, each of which may entail interactive capabilities and intelligence. Due to their close relationship with the build environment, pervasive systems are an ideal domain for adopting a space syntax methodology. The contribution of this paper is two-fold. First, we present an adaptation of the space syntax methodology aimed at researchers and designers of space and pervasive systems. By developing an adaptation of the space syntax methodology we intend for space syntax to be added the arsenal of tools and theories that researchers use to understand and design pervasive systems. To exemplify how this can be achieved, we present three case studies that demonstrate crucial ways in which space syntax analysis can aid the design of pervasive systems. The case studies presented here show how space syntax can be used in the development of pervasive system as an application development tool, as an exploratory tool, and as a modelling tool.

The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems to leverage in a unified framework to boost the performance. Recently, the rapid development of representation learning techniques provides an approach to this problem. By translating the various information sources into a unified representation space, it becomes possible to integrate heterogeneous information for informed recommendation.

In today’s knowledge-driven society, the increasing amount of heterogeneous information, available through a variety of information channels, has made it difficult for users to find the right information at the right time and at the right level of detail. This chapter presents the exploitation of context-awareness in order to reduce the plethora of information that,within the networked enterprise scenario, may confuse and overwhelm the users in need of high-quality information coming from the several available and heterogeneous information sources.

The data from pre, post and during disasters play a very important and vital role while immediate decision making in emergency management. Data availability, analysis, and exchange build up a critical knowledge base that helps in taking the right decision at the right time. Voluminous data is available through various sources in heterogeneous formats. It is a very big challenge for utilization of such massive heterogeneous data in disaster management situations. Disaster data is widely available in various documents and formats. Past disasters, anticipated disasters and during the disasters, a large amount of data is getting exchanged in various documents between different agencies. Effective techniques to extract and gather the data from heterogeneous documents helps in right data extraction and build up a knowledge base to formulate ease of analysis and modeling. Ontology based semantic technology is a powerful mechanism that supports information integration, exchange, share, reuse and build a knowledge base from heterogeneous information sources. The current research work focuses on three aspects of knowledge base system for effective disaster management. The current research work implemented a mechanism to extract relevant information from semi-structured and structured heterogeneous documents using NLP, representing extracted information in homogeneous and machine understandable format using RDF and map the RDF triples to appropriate concepts of the disaster management domain ontologies. This is very novel and effective means of data usage mechanism to help and guide policy makers, disaster mitigation agencies and society at large.

In this paper, we focus on the problem in information integration systems of obtaining data from an heterogeneous data source accurately and effectively. XML is used as data exchange format of the wrapper. We design the wrapper architecture based on the conversion and management of the views as the bridge from global schema to local schema of various data sources. Our wrapper has two main subsystems, the data extract subsystem and query executor subsystem. The former is for loading data for the cache in the mediator when changes more than threshold are detected, and the latter is for answering the query from the mediator. The architecture adapts to the data and schema change of the data source and could answer the query of the mediator effectively. Considering the wrapper may run in the environment without control, the process in the wrapper should be simple enough. The storage in the wrapper itself should be as small as possible and the storage of data source could be used. The detail of the module query rewrite, view management, query merge, result wrap and schema change detect are discussed. The behavior of the wrapper during the query process in the wrapper is discussed with a running example. The security strategy, especially in the distance that the wrapper runs in autonomic data source, is also introduced in this paper.

The rapid growth of the wide area network technology has led to an increasing number of information sources available online. To ensure the query services to scale up with such dynamic open environments, an advanced distributed information system must provide adequate support for dynamic interconnection between information consumers and information producers, instead of just functioning as a static data delivery system. We develop a distributed query scheduling framework to demonstrate the feasibility and the benefit for supporting interoperability and dynamic information gathering across heterogeneous information sources, without relying on an integrated view predefined over the participating information sources. We outline the mechanisms developed for the main components of our distributed query scheduling framework, such as query routing and query execution planning services. We also provide a concrete example to illustrate the issues on how the information consumers' query requests are dynamically processed and linked to the heterogeneous information sources and how the query scheduling framework scales up as the number of information sources increases.

Enterprise information retrieval is quite challenging than web based information retrieval due to retrieval from heterogeneous distributed data sources and need for higher accuracy in retrieval [17]. Large corpus of data from heterogeneous information sources available as distributed systems make querying for most relevant and updated information in less number of trials. Similar users tend to prefer similar search results. Applying three dimensions of user similarity, user-document search history and document similarity, collaborative information fusion based retrieval is proposed in this work. The Fuzzy method for mapping document and search queries, to concept is proposed and using it the similarity between documents is modeled. Active feedback based search result revision is proposed to get highly relevant results in less number of trials.

In many realistic settings of expert finding, the evidence for expertise often comes from heterogeneous knowledge sources. As some sources tend to be more reliable and indicative than the others, d...

With global megatrends like automation and digitization changing societies, economies, and ultimately businesses, shift is underway, disrupting current business plans and entire industries. Business actors have accordingly developed an instinctive fear of economic decline and realized the necessity of taking adequate measures to keep up with the times. Increasingly, organizations find themselves in an evolve-or-die race with their success depending on their capability of recognizing the requirements for serving a specific market and adopting those requirements accurately into their own structure. In the transportation and logistics sector, emerging technological and information challenges are reflected in fierce competition from within and outside. Especially, processes and supporting information systems are put to the test when technological innovation start to spread among an increasing number of actors and promise higher performance or lower cost. As to warehousing, technological innovation continuously finds its way into the premises of the heterogeneous warehouse operators, leading to modifications and process improvements. Such innovation can be at the side of the hardware equipment or in the form of new software solutions. Particularly, the fourth industrial revolution is globally underway. Same applies to Future Internet technologies, a European term for innovative software technologies and the research upon them. On the one hand, new hardware solutions using robotics, cyber-physical systems and sensors, and advanced materials are constantly put to widespread use. On the other one, software solutions based on intensified digitization including new and more heterogeneous sources of information, higher volumes of data, and increasing processing speed are also becoming an integral part of popular information systems for warehouses, particularly for warehouse management systems. With a rapidly and dynamically changing environment and new legal and business requirements towards processes in the warehouses and supporting information systems, new performance levels in terms of quality and cost of service are to be obtained. For this purpose, new expectations of the functionality of warehouse management systems need to be derived. While introducing wholly new solutions is one option, retrofitting and adapting existing systems to the new requirements is another one. The warehouse management systems will need to deal with more types of data from new and heterogeneous data sources. Also, it will need to connect to innovative machines and represent their respective operating principles. In both scenarios, systems need to satisfy the demand for new features in order to remain capable of processing information and acting and, thereby, to optimize logistics processes in real time. By taking a closer look at an industrial use case of a warehouse management system, opportunities of incorporating such new requirements are presented as the system adapts to new data types, increased processing speed, and new machines and equipment used in the warehouse. Eventually, the present paper proves the adaptability of existing warehouse management systems to the requirements of the new digital world, and viable methods to adopt the necessary renovation processes.

In any model the values of estimates for various parameters are obtained from different sources each with its own level of uncertainty. When the probability distributions of the estimates are obtained as opposed to point values only, the measurement uncertainties in the parameter estimates may be addressed. However, the sources used for obtaining the data and the models used to select appropriate distributions are of differing degrees of uncertainty. A hierarchy of different sources of uncertainty based upon one's ability to validate data and models empirically is presented. When model parameters are aggregated with different levels of the hierarchy represented, this implies distortion or degradation in the utility and validity of the models used. Means to identify and deal with such heterogeneous data sourcesare explored, and a number of approaches to addressing this problem is presented. One approach, using Range/confidence Estimates coupled with an Information Value Analysis Process, is presented as an example.

We propose D-uMiddle, a middleware framework that enables devices and sensor nodes in different smart spaces to interact with one another across the Internet. The development of pervasive systems has created smart spaces, where embedded sensors detect user contexts, and computational engines assists user activities. Even though there exists middleware platforms aiming at interoperability among devices, those smart spaces form isolated islands of devices with no uniform protocol for interoperability across smart spaces. This void makes it difficult to develop distributed pervasive computing applications integrating devices and sensor nodes in multiple different smart spaces. D-uMiddle is a framework for achieving such interoperability, and serves as a powerful platform for creating distributed pervasive computing applications that can integrate devices across smart spaces.

The rise of the number and complexity of pervasive systems is a fact. Pervasive systems developers need advanced development methods in order to build better systems in an easy way. Software Factories and the Model Driven Architecture (MDA) are two important trends in the software engineering field. This paper applies the guidelines and strategies described by these proposals in order to build a methodological approach for pervasive systems development. Software Factories are based on the definition of software families supported by frameworks. Individual systems requirements are specified by means of domain specific languages. Following this strategy, our approach defines a framework and a domain specific language for pervasive systems. We use the MDA guidelines to support the development of our domain specific language and the automatic generation of the specific source code of a particular system. The approach presented in this paper raises the abstraction level in the development of pervasive systems and provides high reusable assets to reduce the effort in the development projects.KeywordsModel TransformationGraph TransformationSoftware Product LineGraph GrammarMeeting RoomThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.