Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in realtime based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction.

This paper extends our previous work on deriving meaningful storm patterns from very large rainfall data. In an earlier work, we described MapReduce-based algorithms to identify three types of the storms: local, hourly and overall storms. In general, local storms have temporal characteristics of the storms at a particular site, hourly storms have spatial characteristics of the storms at a particular hour and overall storms have both spatial and temporal characteristics of the storm. We aim to find meaningful patterns and predict trajectories in the spatio-temporal data (i.e. overall storms which are sets of geographically overlapping, consecutive hourly storms). In this paper, we adapt K-Means clustering to find different types of hourly storms based on their shapes and sizes. Since the rainfall data are typically larger than the memory capacity of a single computer, we have implemented this clustering algorithm in Apache Spark, which is a distributed data processing framework, and have run our experiments on a computer cluster.

We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content-rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, GQBE (Graph Query By Example), automatically discovers a weighted hidden maximum query graph based on input query tuples, to capture a user's query intent. It then efficiently finds top-ranked approximate answer graphs and answer tuples.

We witness an unprecedented proliferation of knowledge graphs that record millions of entities and their relationships. While knowledge graphs are structure-flexible and content-rich, they are difficult to use. The challenge lies in the gap between their overwhelming complexity and the limited database knowledge of non-professional users. If writing structured queries over “simple” tables is difficult, complex graphs are only harder to query. As an initial step toward improving the usability of knowledge graphs, we propose to query such data by example entity tuples, without requiring users to form complex graph queries. Our system, Graph Query By Example (GQBE), automatically discovers a weighted hidden maximum query graph based on input query tuples, to capture a user's query intent. It then efficiently finds and ranks the top approximate matching answer graphs and answer tuples. We conducted experiments and user studies on the large Freebase and DBpedia datasets and observed appealing accuracy and efficiency. Our system provides a complementary approach to the existing keyword-based methods, facilitating user-friendly graph querying. To the best of our knowledge, there was no such proposal in the past in the context of graphs.

We present GQBE, a system that presents a simple and intuitive mechanism to query large knowledge graphs. Answers to tasks such as “list university professors who have designed some programming languages and also won an award in Computer Science” are best found in knowledge graphs that record entities and their relationships. Real-world knowledge graphs are difficult to use due to their sheer size and complexity and the challenging task of writing complex structured graph queries. Toward better usability of query systems over knowledge graphs, GQBE allows users to query knowledge graphs by example entity tuples without writing complex queries. In this demo we present: 1) a detailed description of the various features and user-friendly GUI of GQBE, 2) a brief description of the system architecture, and 3) a demonstration scenario that we intend to show the audience.

In our previous work, we described various aspects of our approach in converting big raw rainfall data into meaningful storm concepts. Three concepts were defined: local, hourly, and overall storms. The latter describes overall spatio-temporal characteristics of a storm as it progresses over time. We previously described MapReduce-based algorithms for local and hourly storm identification. Overall storms are the most complex to identify, and are at the core of the storm identification system. Multiple consecutive hourly storms that have spatial overlap are combined to create storm-centric characteristics of the whole storm, which could not be captured in most existing hydrology research. In this paper, we propose a MapReduce-based overall storm identification algorithm, which is based on iteration on MapReduce framework. This greatly improves performance when compared to the depth-first search (DFS) graph traveling approach as introduced in our previous work. In addition, additional essential storm characteristics of hourly and overall storms are introduced in this paper. Examples include storm center concepts for hourly storms and storm track and speed for overall storms.

In this paper, we propose an XML-based recommender system, called PDGP. It is a type of collaborative information filtering system. PDGP uses ontology-driven social networks, where nodes represent social groups. A social group is an entity that defines a group based on demographic, ethnic, cultural, religious, age, or other characteristics. In the PDGP framework, query results are filtered and ranked based on the preferences of the social groups to which the user belongs. The user's social groups are inferred implicitly by the system without involving the user. PDGP constructs the social groups and identifies their preferences dynamically on the fly. These preferences are determined from the preferences of the social groups' member users using a group modeling strategy. PDGP can be used for various practical applications, such as Internet or other businesses that market preference-driven products. We experimentally compared PDGP with an existing system. Results showed marked improvement.

We present in this paper novel context-driven search techniques that compute the semantic similarities among different GO ontology terms. We implemented these techniques in a middleware search engine called GOcSim, which resides between user application and GO database. Most current research is focused on determining semantic similarities between GO ontology terms based solely on their IDs and proximity to one another in GO graph structure, while overlooking the contexts of the terms, which may lead to erroneous results. The context of a term T is determined by the set of other terms, whose existence is dependent on T. We propose novel techniques that determine the contexts of terms based on the concept of existence dependency. We present a stack-based sort-merge algorithm employing these techniques for determining the semantic similarities between GO terms. We evaluated GOcSim experimentally and compared it with four other methods. The results showed marked improvement.

In this paper, we present a novel radio-frequency identification (RFID) smart shelf that accurately locates tagged objects using standard passive UHF RFID tags. This standard-based commercial off-the-shelf approach provides significant advantages over custom HF RFID and other near-field RFID approaches, including reduced tag costs, minimal infrastructure costs, and simple operation. In order to achieve accurate location sensing of objects sitting on the shelf, we utilize a novel localization algorithm that utilizes detected changes in a tag's readability to infer the presence of neighboring tags. According to our experimentation results, with a single RFID reader antenna for two wooden shelves of size 91 cm × 152 cm, our smart-shelf system estimates nine box-level object locations with an average error of just 18.48 cm, which is a 71% improvement in accuracy compared with the previously published k nearest neighbor (KNN) algorithm.

Some types of contextual information are more important than others for inferring a situation of the patient in home-based care. The weight of this information may be changed due to the variations of the sensed values over time. Some researches applied a static weighting based fusion process to their systems to model these variations. However, this fusion process sometimes reduces the reliability of contextual information, since it does not consider a dynamic change of the importance over time. We propose the dynamic weighting based evidential fusion process (DWEFP) as a context reasoning method. DWEFP applies a normalized weighting technique to dynamic evidential networks for improving the confidence level of contextual information. To show the improvement of the proposed method, we compare our DWEFP with previous static weighting based fusion methods such as Bayesian Networks (BNs), Dempster-Shafer Theory (DST), Static Evidential Fusion Process (SEFP), Dynamic Evidential Fusion Process (DEFP), and Dynamic Bayesian Networks (DBNs).

We present in this paper, a context-driven search engine called XCDSearch for answering XML Keyword-based queries as well as Loosely Structured queries, using a stack-based sort-merge algorithm. Most current research is focused on building relationships between data elements based solely on their labels and proximity to one another, while overlooking the contexts of the elements, which may lead to erroneous results. Since a data element is generally a characteristic of its parent, its context is determined by its parent. We observe that we could treat each set of elements consisting of a parent and its children data elements as one unified entity, and then use a stack-based sort-merge algorithm employing context-driven search techniques for determining the relationships between the different unified entities. We evaluated XCDSearch experimentally and compared it with five other search engines. The results showed marked improvement.

In home-based care, reliable contextual information of remotely monitored patients should be generated by correctly recognizing the activities to prevent hazardous situations of the patient. It is difficult to achieve a higher confidence level of contextual information for several reasons. First, low-level data from multisensors have different degrees of uncertainty. Second, generated contexts can be conflicting, even though they are acquired by simultaneous operations. We propose the static evidential fusion process (SEFP) as a context-reasoning method. The context-reasoning method processes sensor data with an evidential form based on the Dezert-Smarandache theory (DSmT). The DSmT approach reduces ambiguous or conflicting contextual information in multisensor networks. Moreover, we compare SEFP based on DSmT with traditional fusion processes such as Bayesian networks and the Dempster-Shafer theory to understand the uncertainty analysis in decision making and to show the improvement of the DSmT approach compared to the others.

Reliable contextual information of remotely monitored patients should be generated to prevent hazardous situations and to provide pervasive services in home-based care. This is difficult for several reasons. First, low level data obtained from heterogeneous sensors have different degrees of uncertainty. Second, generated contexts can be corrupted or conflicted even if they are acquired by simultaneous operations. In this paper, we utilize Dezert-Smarandache theory (DSmT) as an evidence fusion approach to reduce ambiguous or imperfect information then to get higher belief levels in the data fusion process of contextual information. To analyze the improvement of DSmT fusion process, we compare DSmT with Dempster-Shafer theory (DST) using PCR5 rule of combination and Dempster's rule of combination respectively.

In home-based care, reliable contextual information of remotely monitored patients should be generated to recognize activities and to identify hazardous situations of the patient. This is difficult for several reasons. First, low level data obtained from multisensor have different degrees of uncertainty. Second, generated contexts can be conflicting even if they are acquired by simultaneous operations. And last, context reasoning over time is difficult for temporal changes in sensory information. In this paper, we propose the dynamic evidential fusion approach as a context reasoning method in home-based care. The proposed approach processes the generated contexts with Dynamic Evidential Network (DEN), which is composed of the combination of Dezert-Smarandache Theory (DSmT) and Markov Chain (MC). The DSmT reduces ambiguous or conflicting contextual information and the MC processes the association and correlation of sensory information that may change based on time series. Finally, we compare the dynamic evidential fusion approach with the static evidential fusion approach for analyzing the improvement of the dynamic evidential fusion approach.

OOXKSearch is a semantic search engine that answers XML keyword-based queries as well as loosely structured queries using Object Oriented techniques. There has been extensive research in XML keyword-based and loosely structured querying. Some frameworks work well for certain types of XML data models while fail in others. The reason is that the proposed techniques are based solely on establishing relationships between individual elements while overlooking the context of these elements. The context of a data element is determined by its parent, because it specifies one of the characteristics of the parent. Since data elements are nothing but characteristics of their parents, we observe that we could treat each parent-children set of elements as one unified entity. We then find semantic relationships between the different unified entities. If two distinct unified entities are semantically related, their data elements are also semantically related. The search performance and quality of OOXKSearch were evaluated experimentally and compared with three recent proposed systems. The results showed marked improvement.

In biomedical/biological research fields, some semantic data (experiments, evidence, annotation, etc.) can be classified into different levels (from the DNA/RNA level to higher levels such as cells, tissues, organs, and biological systems) based on different degrees of abstraction. Hence, it is important to represent and integrate them across different levels. The purpose of this paper is to address the characteristics of biological data and develop a multi-level conceptual model. Our approach facilitates more accurate modeling of biomedical ontologies, and a better understanding of the data stored in various biological data sources. Furthermore, we propose intra-level and inter-level relationships and give formal definitions illustrated with examples in order to precisely describe relation/behavior among concepts at multiple levels. Our work can be used as the building block for biomedical data or ontologies integration.

Biological data such as protein structure and function, DNA sequences, and metabolic pathways require conceptual modeling characteristics that are not available in the widely used ER (entity-relationship) model and its variant the EER (enhanced-ER) model. In this paper, we suggest a solution to this problem, requiring only minimal changes to the EER model by introducing specialized formal relationships for ordering, input/output processes and spatial structure. These changes facilitate more accurate modeling of biological ontologies, their structures, and mediator systems. We propose new EER schema constructs to represent the ordering of DNA sequences, the structure of proteins, and the processes of metabolic pathways

Summary form only given. Biological data such as nucleic acids (DNA or RNA), proteins, and pathways require certain conceptual modeling characteristics that are not available in the popular ER (entity-relationship) model and its variations, such as the EER (extended-ER) models. In this paper, we try to make minimal changes to the EER model by introducing special relationships for ordering and process input/output. This allows more accurate modeling of bioinformatics structures that can be used in bioinformatics ontologies and mediator systems. We propose some EER schemas for DNA sequence ordering, the structures of proteins, and pathway processes

Summary form only given. Over the past few years, the development of technology has allowed new advances in sensor networks that monitor the physical world. The data streams produced by sensor networks have different characteristics from the data of traditional data processing, thus requiring new paradigms of data processing systems. Recently, a lot of research has been reported on data stream processing systems. However, there are many underlying assumptions about these systems that have not been explicitly specified. In this paper, our attempt is to provide a general model and architecture for data stream processing in sensor networks. This can serve as a reference architecture to better understand and categorize research in this area.

In this paper, we propose a protocol, called asynchronous data delivery protocol (AD/sup 2/P), in multi-hop wireless networks for processing time-uncritical data which needs to be stored in or retrieved from a database in the Internet using the existing TCP-based applications.

In this paper, we demonstrate how simulated annealing algorithm can be applied to clustering algorithms used in ad hoc networks; specifically our recently proposed weighted clustering algorithm (WCA) is optimized by simulated annealing. As the simulated annealing stands to be a powerful stochastic search method, its usage for combinatorial optimization problems was found to be applicable in our problem domain. The problem formulation along with the parameters is mapped to be an individual solution as an input to the simulated annealing algorithm. Input consists of a random set of clusterhead set along with its members and the set of all possible dominant sets chosen from a given network of N nodes as obtained from the original WCA. Simulated annealing uses this information to find the best solution defined by computing the objective function and obtaining the best fitness value. The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the MAC protocol. Consequently, it results in the minimum number of clusters and hence clusterheads. Simulation results exhibit improved performance of the optimized WCA than the original WCA.

We show how genetic algorithms can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. In particular, we optimize our recently proposed weighted clustering algorithm (WCA). The problem formulation along with the parameters are mapped to individual chromosomes as input to the genetic algorithmic technique. Encoding the individual chromosomes is an essential part of the mapping process; each chromosome contains information about the clusterheads and the members thereof, as obtained from the original WCA. The genetic algorithm then uses this information to obtain the best solution (chromosome) defined by the fitness function. The proposed technique is such that each clusterhead handles the maximum possible number of mobile nodes in its cluster in order to facilitate the optimal operation of the medium access control (MAC) protocol. Consequently, it results in the minimum number of clusters and hence clusterheads. Simulation results exhibit improved performance of the optimized WCA than the original WCA. Moreover, the loads among clusters are more evenly balanced by a factor of ten.

In this paper we show that the language of declarative logic programming (DLP) with answer sets and its extensions can be used to specify database evolution due to updates and active rules, and to verify correctness of active rules with respect to a specification described using temporal logic and aggregate operators. We classify the specification of active rules into four kind of constraints which can be expressed using a particular extension of DLP called Smodels. Smodels allows us to specify the evolution, to specify the constraints, and to enumerate all possible initial database states and initial updates. Together, these can be used to analyze all possible evolution paths of an active database system to verify if they satisfy a set of given constraints.

Virtual Reality Modeling Language (VRML) is widely used to represent, create, and display virtual reality objects and their environment. Some VRML applications require concurrent interaction by multiple users in a real-time distributed fashion. Such applications need a method for users to share and update the VRML objects in real-time. To allow concurrent shared real-time access, our approach is to store the VRML objects in an object-oriented database system (ObjectStore) in order to utilize the concurrency control mechanisms of the system. The authors present an architecture that allows multiple users to interact in a non-trivial way in such a shared VRML environment. We outline how the VRML world can be saved in ObjectStore and implement a series of test cases demonstrating concurrency issues arising from simultaneous updates. Our architecture uses ordinary Java enabled Web browsers with a VRML plug-in. A commercial Web server routes client requests to a custom application server which interacts with the object-oriented database. As users change the VRML world, our application server orders the requests and updates the master copy in the database.

There have been many temporal data models proposed in the literature. Most of them are based on relational models. Despite their popularity as design and analysis tools for information systems, entity-relationship (ER) based temporal data models have not drawn as much attention as those based on relational models. One reason is that most ER-based temporal data models lack an underlying formalism and algebra. If a conceptual model, along with an algebra, is formally defined, we can design a query language that operates on the conceptual model rather than on the implementation model. Also, it can provide a basis for a user-friendly visual query language. In this paper, we present a temporal algebra for an ER-based temporal data model called ITDM (Integrated Temporal Data Model). We define ten algebraic operations and ten temporal aggregate operations. We also define time-series-specific operations.

The explosive growth of data on the World Wide Web makes information management and knowledge discovery increasingly difficult. Applying database techniques to manage Web information can help in solving these problems. One difficulty encountered is that Web documents, unlike structured databases, contain unstructured and semi-structured data. Our hypothesis is that creating ontologies to describe the semantics of Web data is the key to bridging the gap between semi-structured data and structured databases, and hence to facilitating the application of database techniques. We extract an ontology (or conceptual schema) from a set of Web pages in a particular application domain automatically. The prototype we are constructing is called WebOntEx (Web Ontology Extraction). This paper describes the data preparation process and the semantic resolution process of the WebOntEx project to build a meta-database and a Web database.

Bi temporal databases allow users to record retroactive (past) and proactive (future planned) versions of an entity, and to retrieve the appropriate versions for bi temporal queries that involve both valid time and transaction time. Currently used timestamp representations are mainly for either valid time or transaction time databases. We first categorize the types of problems that can occur in existing models. These are: ambiguity; priority specification; and lost information. We then propose a 2TDB model that allows both retroactive and proactive versions, overcomes the identified problems, and permits the correction of recorded facts.

Time is an attribute of all real-world phenomena and many real-world database applications naturally involve time-varying information. As in traditional databases, joins are the most important and costly operators in temporal databases. Hence, efficient processing strategies for temporal joins are needed. The Entity Join operation is used to group temporal attributes of an entity into a single relation, and is important because those temporal attributes are likely to reside in separate relations due to normalization. In this paper, we propose an efficient Entity-join algorithm for both non-ordered and append-only temporal databases. The proposed approach can be applied to other temporal join operations, such as valid-time equijoin.

A methodology for detecting failures that occur in distributed computer systems connected by a communications network is described. The methodology utilizes active polling of monitored systems. The entities polled must be service entities that function at the application layers of service providing machines. A prototype system has been implemented to test this methodology.

Article Free Access Share on The time index+: an incremental access structure for temporal databases Authors: Vram Kouramajian Department of Computer Science, The Wichita State University, Wichita, Kansas Department of Computer Science, The Wichita State University, Wichita, KansasView Profile , Ibrahim Kamel Department of Computer Science, University of Maryland, College Park, Maryland Department of Computer Science, University of Maryland, College Park, MarylandView Profile , Ramez Elmasri Computer Science Engineering Department, The University of Texas at Arlington, Arlington, Texas Computer Science Engineering Department, The University of Texas at Arlington, Arlington, TexasView Profile , Syed Waheed Computer Science Engineering Department, The University of Texas at Arlington, Arlington, Texas Computer Science Engineering Department, The University of Texas at Arlington, Arlington, TexasView Profile Authors Info & Claims CIKM '94: Proceedings of the third international conference on Information and knowledge managementNovember 1994 Pages 296–303https://doi.org/10.1145/191246.191298Online:29 November 1994Publication History 17citation423DownloadsMetricsTotal Citations17Total Downloads423Last 12 Months6Last 6 weeks1 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper addresses the issues of declustering temporal index and access structures for a single processor multiple independent disk architecture. The temporal index is the Monotonic B/sup +/-Tree which uses the time index temporal access structure. We devise a new algorithm, called multi-level round robin, for assigning tree nodes to multiple disks. The multi-level round robin declustering technique takes advantage of the append-only nature of temporal databases to achieve uniform load distribution, decrease response time, and increase the fanout of the tree by eliminating the need to store disk numbers within the tree nodes. We propose two declustering techniques for the time index access structures; one considers only time proximity while declustering, whereas the other considers both time proximity and data size. We investigate their performance over different types of temporal queries and show that various temporal queries have conflicting allocation criteria for the time index buckets. In addition, we devise two disk partition techniques for the time index buckets. The mutually exclusive technique partitions the disks into disjoint groups, whereas the shared disk technique allows the different types of buckets to share all disks.>

Article Free Access Share on Temporal database modeling: an object-oriented approach Authors: Ramez Elmasri Computer Science Engineering Department, The University of Texas at Arlington, Arlington, Texas Computer Science Engineering Department, The University of Texas at Arlington, Arlington, TexasView Profile , Vram Kouramajian Office of The Vice President for Research & Information Technology, Rice University, P.O. Box 1892, Houston, Texas Office of The Vice President for Research & Information Technology, Rice University, P.O. Box 1892, Houston, TexasView Profile , Shian Fernando Computer Science Engineering Department, The University of Texas at Arlington, Arlington, Texas Computer Science Engineering Department, The University of Texas at Arlington, Arlington, TexasView Profile Authors Info & Claims CIKM '93: Proceedings of the second international conference on Information and knowledge managementDecember 1993 Pages 574–585https://doi.org/10.1145/170088.170429Published:01 December 1993Publication History 6citation841DownloadsMetricsTotal Citations6Total Downloads841Last 12 Months10Last 6 weeks4 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

The authors present a storage model for temporal databases that accommodates large amounts of temporal data. The model supports efficient search for object versions based on temporal conditions, using a time index. They define an access structure, the monotonic B/sup +/-tree, that is suitable for implementing a time index for append-only temporal databases. The storage model uses a combination of magnetic disks and write-once optical disks to keep current, past, and even future states of a database online and readily accessible. It provides an automatic archiving of both object versions and time index blocks to optical disks.>

A new indexing technique, time index, for improving the performance of certain classes of temporal queries was previously described by the author (see The 16th Conference on Very Large Databases (1990)). Three variations for implementing the time index efficiently are proposed and the performance of these three variations is compared with the performance of the original time index. Various parameters such as average lifetime of a version, average number of versions per object, block size and block clustering, and query time interval length are discussed. Simulation results show how these parameters affect the performance of the various implementation variations for the time index.>

A temporal extension to an extended ER (entity-relationship) model is proposed. The concept of life span for entities and relationships is incorporated in the ER model. Also specified is a temporal query and update language which is an extension of the GORDAS language. The concepts of temporal Boolean expressions, temporal selection, and temporal projection are introduced, and their use in the query language is illustrated. Temporal constraints are incorporated into the ER model, and the semantics of temporal update operations is defined.>

The authors present a common foundation for integrating pairs of entity sets, pairs of relationship sets, and an entity set with a relationship set. This common foundation is based on the basic principle of integrating attributes. Any pair of objects whose identifying attributes can be integrated can themselves be integrated. Several definitions of attribute equivalence are presented. These definitions can be used to specify the exact nature of the relationship between a pair of attributes. Based on these definitions, several strategies for attribute integration are presented and evaluated.>

Fragmentation and query decomposition are considered important issues in a distributed database environment. For a semantic data model (ECR data model), the types of fragmentation and the criteria they should follow is provided along with how global queries in a high-level language, GORDAS, are decomposed into local queries on the fragments. This technique may be used in a heterogeneous distributed database, where the global schema presented to the user is in the high-level ECR model.

View integration is one of the important phases in logical database design. During this phase, the individual views designed by separate user groups within the organization are integrated into a conceptual schema for the entire organization. In this paper, we present rules for some of the aspects of view integration, namely integration of entity classes in different views. We then compare our rules with previous approaches, and discuss some of the problems which have to be solved for other aspects of view integration.

One approach to the design of a logical model for an integrated database requires each potential user or application to specify its view as a data model. An integration phase follows, where these user data models are integrated into a global database model. We address the problem of view integration when user data models are expressed using the structural model [Wi77, WE79].The structural model is built from relations in Boyce-Codd normal form [Co74]. A basic set of integrity assertions is implicit in the model. The integrity assertions are defined by classification of relations into types, and are represented by connections between relations. We will show how to integrate different representations of two related real-world entity classes.