Data model for warehousing historical Web information

We examine new I/O-efficient techniques for indexing problems in constraint and temporal data models. We present algorithms for these problems that are considerably simpler than previous solutions. Our solutions are unique in the sense that they only use B+-trees rather than special-purpose data structures. Indexing for many general constraint data models can be reduced to interval intersection. We present a new algorithm for this problem using a query-time/space tradeoff, which achieves the optimal query time O(logB n+t/B) I/O's in linear space O(n/B) using B+-trees. (Here, n is the number of intervals, t the number of intervals in the output of a query, and B the disk block size.) It is easy to update this data structure, but small worst-case bounds do not seem possible. Previous approaches have achieved these bounds but are fairly complex and rely mostly on reducing the interval intersection problem to special cases of two-dimensional search. Some of them can also handle updates in O(logB n) I/O's amortized. Indexing in many temporal models becomes a generalization of interval management, where each temporal object is characterized by an interval and a key. There are many different ways of querying these objects, and we achieve optimal bounds for many of these queries. These bounds are achieved using a modification of the technique used for the constraint indexing problem. Our technique is much simpler than other techniques that have been used for achieving similar bounds.KeywordsQuery PointTemporal IndexingDisk AccessTemporal ObjectSecondary StorageThese 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.

In the field of databases, time management at different levels of granularity has been an issue for several years, for instance when dealing with clinical information from different databases using different time units, dealing with natural language expressions, or when dealing with temporal uncertainty. A temporal data model is proposed to manage the temporal aspect of data, presented at various and mixed levels of granularity. The concept of temporal assertions shapes the entire temporal information. The model provides a temporal dimension to the data by using intervals that can be specified at different granularities. The model supports a three-valued logic, where True, False and Undefined are the truth values. The temporal data model allows to manage some degrees of uncertainty when establishing temporal relationships between intervals or between temporal assertions, expressed at different granularities. The logical connectives and quantifiers can manage each of the three truth-values. We applied the temporal data model by implementing an object-oriented database system for managing follow-up clinical data from patients who underwent percutaneous transluminal coronary angioplasty.

We address the key issues of temporal data modeling and temporal integrity constraints in the temporal extensions to the relational data model in attribute and tuple time stamping. These issues include representation of temporal data, temporal grouping identifiers, primary keys of temporal relations and other forms of temporal integrity constraints. We extend the traditional integrity constraints to the temporal domain. In tackling subtle issues temporality creates, we conceptualize that a temporal integrity constraint has a data and a temporal (time) component. The time component of a temporal integrity constraint limits the attribute values to the lifespan of an object (tuple). We also generalize single state integrity constraints to multi-state temporal integrity constraints and apply them to existential and referential integrity. We provide algorithms for enforcing the two types of temporal integrity constraints.

Commercial OLAP systems usually consider OLAP dimensions as static entities. In practice, dimension updates are often necessary in order to adapt the multidimensional database to changing requirements. Query languages are then needed to retrieve historical information from a data warehouse. Although many proposals addressing temporal OLAP exist, none supports full operation over the World Wide Web. After introducing a temporal data model supporting historical dimensions and fact table versioning, we present a three-tier architecture based on web services, SOAP and XML, allowing efficient querying over the web. In this architecture, XML metadata is stored at the application server in the form of XML documents containing the data warehouse structure. This allows addressing most of the requests (navigation through time, updates and queries) without accessing the data warehouse. In the second part of the paper, we present TOLAP-QL, an SQL-like temporal query language. We describe the syntax and semantics of the language, a sequel of its logic-based predecessor, TOLAP. We study query optimisation in temporal OLAP, and introduce a technique based on materialisation of historical views. Last, but not least, we present a system implementation, and a real-world case study, that shows that our proposal achieves good query performance for OLAP queries over large historical databases in a web environment.

We show how to detect and represent Web deltas, i.e., changes in Web information, that are relevant to a user's query in the context of our Web warehousing system called WHOWEDA (Warehouse of Web Data). In WHOWEDA, Web information are materialized views stored in Web tables and can be manipulated and analyzed using a set of Web algebraic operators. We present a mechanism to detect relevant Web deltas using Web join and outer Web join. We show how to represent these changes using delta Web tables.

An XML-based methodology for parametric temporal database model implementation

In ubiquitous environments, many applications need to process data with time and space dimensions. Because of this, there is growing attention not only on gathering spatiotemporal data in ubiquitous environments, but also on processing such data in databases. In order to obtain the full benefits from spatiotemporal data, we need a data model that naturally expresses the properties of spatiotemporal data. In this paper, we introduce three spatiotemporal data models extended from temporal data models. The main goal of this paper is to determine which data model is less complex in the spatiotemporal context. To this end, we compare their query languages in the complexity aspect because the complexity of a query language is tightly coupled with its underlying data model. Throughout our investigations, we show that it is important to intertwine space and time dimensions and keep one-to-one correspondence between an object in the real world and a tuple in a database in order to naturally express queries in ubiquitous applications.

Data warehousing is an emerging technology that greatly extends the capabilities of relational databases specifically in the analysis of very large sets of time-oriented data. The emergence of data warehousing has been somewhat eclipsed over the past decade by the simultaneous emergence of Web technologies. However, Web technologies and data warehousing have some natural synergies that are not immediately obvious. First, Web technologies make data warehouse data more easily available to a much wider variety of users. Second, data warehouse technologies can be used to analyze traffic to a Web site in order to gain a much better understanding of the visitors to the Web site. It is this second synergy that is the focus of this article.

To analyze the temporal aspects of symptoms, including their temporal uncertainty, in order to develop a high-level conceptual data model representation of this domain. A basic tenet of existing temporal models is that events occur not only relative to a particular date or time, but also relative to the time of some other event. The time an event occurs, particularly when the event is a symptom being recalled by a patient or collected by a busy provider, is frequently incomplete or uncertain, and this uncertainty must also be represented in a temporal data model. The object-oriented modeling technique used in this study is becoming popular among U.S. medical informatics standards developers. A conceptual data model for the temporal aspects of symptom data, including temporal uncertainty, has been developed. The object-oriented modeling approach used enables the temporal objects and attributes defined in this model to be inherited by other medical objects, such as problems. The temporal comparators presented here have previously been defined, and may serve as the basis for standardizing the terms used to describe how one event temporally relates to another. In an attempt to achieve domain completeness, this study concentrated more on developing a model that is highly expressive than on developing one that is easily queried. This trade-off in representation versus "queryability" will require further analysis and may require modifications to the underlying model.

Databases in general store current data. However, the capability to maintain temporal data is a crucial requirement for many organizations and provides the base for organizational intelligence. A temporal database has a time dimension and maintains time-varying data (i.e., past, present, and future data). In this article, we focus on the relational data model and address the subtle issues in modeling temporal data, such as comparing database states at two different time points, capturing the periods for concurrent events, and accessing to times beyond these periods, handling multivalued attributes, coalescing, and restructuring temporal data (Gadia 1988, Tansel & Tin, 1997). Many extensions to the relational data model have been proposed for handling temporal data.

We propose constraint databases as an intermediate level facilitating the interoperability of spatiotemporal data models. Constraint query languages are used to express translations between different data models. We illustrate our approach in the context of a number of temporal, spatial, and spatiotemporal data models.

Semantic mashup of biomedical data

The Semantic Web (SW) has drawn the attention of data enthusiasts, and also inspired the exploitation and design of multidimensional data warehouses, in an unconventional way. Traditional data warehouses (DW) operate over static data. However multidimensional (MD) data modeling approach can be dynamically extended by defining both the schema and instances of MD data as RDF graphs. The importance and applicability of MD data warehouses over RDF is widely studied yet none of the works support a spatially enhanced MD model on the SW. Spatial support in DWs is a desirable feature for enhanced analysis, since adding encoded spatial information of the data allows to query with spatial functions. In this paper we propose to empower the spatial dimension of data warehouses by adding spatial data types and topological relationships to the existing QB4OLAP vocabulary, which already supports the representation of the constructs of the MD models in RDF. With QB4SOLAP, spatial constructs of the MD models can be also published in RDF, which allows to implement spatial and metric analysis on spatial members along with OLAP operations. In our contribution, we describe a set of spatial OLAP (SOLAP) operations, demonstrate a spatially extended metamodel as, QB4SOLAP, and apply it on a use case scenario. Finally, we show how these SOLAP queries can be expressed in SPARQL.

Semantic Web is the name of the next generation World Wide Web, that has been recently proposed by Tim Berners-Lee and the World Wide Web Consortium (W3C)1. In this new Web architecture, information and Web services will be easily understandable and usable by both humans and computers. The objective is not to make computers understand the human language, but to define a universal model for the expression of the information and a set of inference rules that machines can easily use in order to process and relate the information as if they really understood it (Berners-Lee, 1998). Though, as the current Web provided sharing of documents among previously incompatible computers, the Semantic Web intends to go beyond, allowing stovepipe systems, hardwired computers, and other devices to share contents embedded in different documents. The most known architecture for Semantic Web is based on a stack of related technologies, each one being a whole research area by itself (Berners-Lee, Hendler, & Lassila. 2001; Pereira & Freire, 2005). Accomplishment of the Semantic Web is considered a great challenge, not only due to the complexity of implementation but also because of the vast applicability in several areas. In spite of this, Semantic Web is still one of the most promising research areas among those which aim to define a new architecture for the Web. Semantic Web goes far beyond previous information retrieval and knowledge representation projects, presenting a non-centralized way to represent and contextualize real-world concepts, unambiguously, for several areas of knowledge. Semantic Web-enabled machines will handle information at our communication level. It is clear that the ability to interpret reality is still very primitive, however, Semantic Web points a way towards machine interaction and learning (Pereira et al., 2005). Semantic Web will integrate, interact with, and bring benefits to most human activities. Its full potential will go beyond the Web to real-world machines, providing increased interaction between machines and with humans—smarter phones, radios, and other electronic devices. Semantic Web will bring a different kind of approach in the understanding of reality by the machines and will constitute a mark in the evolution of human knowledge (Pereira et al., 2005).

In the past few years, several conceptual approaches have been proposed for the specification of the main multidimensional (MD) properties of the data warehouse (DW) repository. However, most of them deal with isolated aspects of the DW and do not provide designers with an integrated and standard method for designing the whole DW life cycle (ETL processes, data sources, DW repository and so on).Some approaches are depending on specific platform or neglecting important issues in DW design life cycle. Extraction-transformation-loading (ETL) processes play an important role in data warehouse architecture because they are responsible of integrating data from heterogeneous data sources into the DW repository. This paper proposes a conceptual model to refresh data warehouse by (insert, update, delete) data using ETL processes and considering DW security requirements. Firstly, the proposed ETL model is based on Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of ETL processes .secondly; this part focuses on how to integrate the proposed ETL model with the DW model. The proposed DW model depends on the Model Driven Architecture (MDA). MDA is a standard framework for software development that addresses the complete life cycle of designing, deploying, integrating, and managing applications by using models in software development. This paper proposes an integrated conceptual model for addressing temporal data warehouse security requirements (CMTDWS).