Deep clustering (DC), a fusion of deep representation learning and clustering, has recently demonstrated positive results in data science, particularly text processing and computer vision. However, joint optimization of feature learning and data distribution in the multi-dimensional space is domain-specific, so existing DC methods struggle to generalize to other application domains (such as data integration). In data management tasks, where high-density embeddings and overlapping clusters dominate, a data management-specific DC algorithm should be able to interact better with the data properties to support data integration tasks. This paper presents a deep clustering algorithm for tabular data (TableDC) that reflects the properties of data management applications that cluster tables (schema inference), rows (entity resolution) and columns (domain discovery). To address overlapping clusters, TableDC integrates Mahalanobis distance, which considers variance and correlation within the data, offering a similarity method suitable for tabular data in high-dimensional latent spaces. TableDC also shows higher tolerance to outliers through its heavy-tailed Cauchy distribution as the similarity kernel. The proposed similarity measure is particularly beneficial where the embeddings of raw data are densely packed and exhibit high degrees of overlap. Data integration tasks may also involve large numbers of clusters, which challenges the scalability of existing DC methods. TableDC learns data embeddings with a large number of clusters more efficiently than baseline DC methods, which scale in quadratic time. We evaluated TableDC with several existing DC, Standard Clustering (SC), and state-of-the-art bespoke methods over benchmark datasets. TableDC consistently outperforms existing DC, SC and bespoke methods.

Abstract Cloud computing is an established paradigm for end users to access resources. Cloud infrastructure providers seek to maximize accepted requests, meet Service Level Agreements (SLAs), and reduce operational costs by dynamically allocating Virtual Machines (VMs) to physical nodes. Many solutions have been presented to manage cloud infrastructure, however, these tend to be centralized and suffer in their ability to maintain Quality of Service (QOS) and support data centers with thousands of nodes. Decentralized approaches, with no central management, can manage large data centers. However, these tend to reduce the ability to obtain an optimal resource allocation across the data center. To address this, we propose a hybrid hierarchical decentralized architecture that achieves lower SLA violations and lowers network traffic. We used simulation to evaluate our proposal in practice with a variety of existing VM placement policies.

Accurately identifying different representations of the same real-world entity is an integral part of data cleaning and many methods have been proposed to accomplish it. The challenges of this entity resolution task that demand so much research attention are often rooted in the task-specificity and user-dependence of the process. Adopting deep learning techniques has the potential to lessen these challenges. In this paper, we set out to devise an entity resolution method that builds on the robustness conferred by deep autoencoders to reduce human-involvement costs. Specifically, we reduce the cost of training deep entity resolution models by performing unsupervised representation learning. This unveils a transferability property of the resulting model that can further reduce the cost of applying the approach to new datasets by means of transfer learning. Finally, we reduce the cost of labeling training data through an active learning approach that builds on the properties conferred by the use of deep autoencoders. Empirical evaluation confirms the accomplishment of our cost-reduction desideratum, while achieving comparable effectiveness with state-of-the-art alternatives.

The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, the steps in such a process are carried out using Extract-Transform-Load platforms, with significant manual involvement in specifying, configuring or tuning many of them. In typical big data applications, we need to ensure that all wrangling steps, including web extraction, selection, integration and cleaning, benefit from automation wherever possible. Towards this goal, in the paper we: (i) introduce a notion of data context, which associates portions of a target schema with extensional data of types that are commonly available; (ii) define a scalable methodology to bootstrap an end-to-end data wrangling process based on data profiling; (iii) describe how data context is used to inform automation in several steps within wrangling, specifically, matching, value format transformation, data repair, and mapping generation and selection to optimise the accuracy, consistency and relevance of the result; and (iv) we evaluate the approach with real estate data and financial data, showing substantial improvements in the results of automated wrangling.

Schema mapping generation identifies how data sets can be combined to create views that are relevant to an application. Where the data sets to be combined lack declared relationships, such as foreign keys, schema mapping generation can be considered to be in the wild. In this paper, we describe an approach to schema mapping generation in the context of open government data, in particular, the London Datastore. Mapping generation is informed by inferred profiling data about the data sets and their relationships, where the data sets are made available as csv files. We outline the mapping generation algorithm, and describe a demonstration of the approach, in which the user can: (i) specify the target to be populated by the generated mappings over a collection of sources from The London Datastore; (ii) browse the generated candidate mappings and the evidence that informed their creation; and (iii) steer the mapping generation process, to make use of preferred sources and dependable profiling results.

Data analytics stands to benefit from the increasing availability of datasets that are held without their conceptual relationships being explicitly known. When collected, these datasets form a data lake from which, by processes like data wrangling, specific target datasets can be constructed that enable value- adding analytics. Given the potential vastness of such data lakes, the issue arises of how to pull out of the lake those datasets that might contribute to wrangling out a given target. We refer to this as the problem of dataset discovery in data lakes and this paper contributes an effective and efficient solution to it. Our approach uses features of the values in a dataset to construct hash- based indexes that map those features into a uniform distance space. This makes it possible to define similarity distances between features and to take those distances as measurements of relatedness w.r.t. a target table. Given the latter (and exemplar tuples), our approach returns the most related tables in the lake. We provide a detailed description of the approach and report on empirical results for two forms of relatedness (unionability and joinability) comparing them with prior work, where pertinent, and showing significant improvements in all of precision, recall, target coverage, indexing and discovery times.

When procuring or administering any I.T. system or a component of an I.T. system, it is crucial to understand the computational resources required to run the critical business functions that are governed by any Service Level Agreements. Predicting the resources needed for future consumption is like looking into the proverbial crystal ball. In this paper we look at the forecasting techniques in use today and evaluate if those techniques are applicable to the deeper layers of the technological stack such as clustered database instances, applications and groups of transactions that make up the database workload. The approach has been implemented to use supervised machine learning to identify traits such as reoccurring patterns, shocks and trends that the workloads exhibit and account for those traits in the forecast. An experimental evaluation shows that the approach we propose reduces the complexity of performing a forecast, and accurate predictions have been produced for complex workloads.

Format transformation is one of the most labor intensive tasks of a data wrangling process. Recent advances in programming by example proposed synthesis algorithms that showed promising results on spreadsheet data. However, when employed on repositories consisting of multiple sources and large number of examples, such algorithms manifest scalability issues. This paper introduces a new transformation synthesis technique based on edit operations that enables efficient learning of transformation programs. Empirical results show comparable effectiveness and dramatic improvements in efficiency over the state-of-the art.

Schema mappings enable declarative and executable specification of transformations between different schematic representations of application concepts. Most work on mapping generation has assumed that the source and target schemas are well defined, e.g., with declared keys and foreign keys, and that the mapping generation processes exist to support the data engineer in the labour-intensive process of producing a high-quality integration. However, organizations increasingly have access to numerous independently produced data sets, e.g., in a data lake, with a requirement to produce rapid, best-effort integrations, without extensive manual effort. This paper introduces Dynamap, a mapping generation algorithm for such settings, where metadata about sources and the relationships between them is derived from automated data profiling, and where there may be many alternative ways of combining source tables. Our contributions include a dynamic programming algorithm for exploring the space of potential mappings, and techniques for propagating profiling data through mappings, so that the fitness of candidate mappings can be estimated. Experimental results show the effectiveness and scalability of the approach in a variety of synthetic and real-world scenarios.

Abstract Data wrangling is the process whereby data are cleaned and integrated for analysis. Data wrangling, even with tool support, is typically a labour intensive process. One aspect of data wrangling involves carrying out format transformations on attribute values, for example so that names or phone numbers are represented consistently. Recent research has developed techniques for synthesizing format transformation programs from examples of the source and target representations. This is valuable, but still requires a user to provide suitable examples, something that may be challenging in applications in which there are huge datasets or numerous data sources. In this paper, we investigate the automatic discovery of examples that can be used to synthesize format transformation programs. In particular, we propose two approaches to identifying candidate data examples and validating the transformations that are synthesized from them. The approaches are evaluated empirically using datasets from open government data.

Data scientists are usually interested in a subset of sources with properties that are most aligned to intended data use. The SOURCERY system supports interactive multi-criteria user-driven source selection. SOURCERY allows a user to identify criteria they consider of importance and indicate their relative importance, and seeks a source selection result aligned to the user-supplied criteria preferences. The user is given an overview of the properties of the sources that are selected along with visual analyses contextualizing the result in relation to what is theoretically possible and what is possible given the set of available sources. The system also enables a user to interactively perform iterative fine-tuning to explore how changes to preferences may impact results.

Source selection is the problem of identifying a subset of available data sources that best meet a user’s needs. In this paper we propose a user-driven approach to source selection that seeks to identify sources that are most fit for purpose. The approach employs a decision support methodology to take account of a user’s context, to allow end users to tune their preferences by specifying the relative importance between different criteria, looking to find a trade-off solution aligned with his/her preferences. The approach is extensible to incorporate diverse criteria, not drawn from a fixed set, and solutions can use a subset of the data from each selected source, rather than require that sources are used in their entirety or not at all. The paper describes and motivates the approach, presenting a methodology for modelling a user’s context, and its collection of optimisation algorithms for exploring the space of solutions, and compares and evaluates the resulting algorithms using multiple real world data sets. The experiments show how source selection results are produced that are attuned to each user’s preferences, both with respect to overall weighted utility and through faithful representation of a user’s preferences within a result, while scaling to potentially thousands of sources.

The process of preparing potentially large and complex data sets for further analysis or manual examination is often called data wrangling. In classical warehousing environments, the steps in such a process have been carried out using Extract-Transform-Load platforms, with significant manual involvement in specifying, configuring or tuning many of them. Cost-effective data wrangling processes need to ensure that data wrangling steps benefit from automation wherever possible. In this paper, we define a methodology to fully automate an end-to-end data wrangling process incorporating data context, which associates portions of a target schema with potentially spurious extensional data of types that are commonly available. Instance-based evidence together with data profiling paves the way to inform automation in several steps within the wrangling process, specifically, matching, mapping validation, value format transformation, and data repair. The approach is evaluated with real estate data showing substantial improvements in the results of automated wrangling.

A promising approach to increase the efficiency of infrastructure usage is to adapt the assignment of resources to workloads. This can be used, for example, to consolidate existing workloads so that the new capability can be used to serve new requests, or alternatively unused resources may be turned off to reduce energy consumption. Many architectural solutions have been presented for data centre management, however these tend to be centralised and may suffer in their ability to scale and support data centres with tens of thousands of nodes. Distributed approaches solve the scalability problem, however these do not have a global view of resources across the data centre. To address this, we propose a novel hybrid distributed hierarchical framework that is effective at providing the information needed for decision making at scale. We evaluate the performance of our approach by simulation, and demonstrate that a hybrid approach is a viable solution for managing large data centres, through rapid information dissemination and ability to make decisions using a global view.

Data wrangling, the multi-faceted process by which the data required by an application is identified, extracted, cleaned and integrated, is often cumbersome and labor intensive. In this paper, we present an architecture that supports a complete data wrangling lifecycle, orchestrates components dynamically, builds on automation wherever possible, is informed by whatever data is available, refines automatically produced results in the light of feedback, takes into account the user's priorities, and supports data scientists with diverse skill sets. The architecture is demonstrated in practice for wrangling property sales and open government data.

Data wrangling is the process by which the data required by an application is identified, extracted, cleaned and integrated, to yield adata set that is suitable for exploration and analysis. Although there are widely used Extract, Transform and Load (ETL) techniques and platforms, they often require manual work from technical and domain experts at different stages of the process. When confronted with the 4 V’s of big data (volume, velocity, variety and veracity),manual intervention may make ETL prohibitively expensive. This paper argues that providing cost-effective, highly-automated approaches to data wrangling involves significant research challenges,requiring fundamental changes to established areas such as data extraction,integration and cleaning, and to the ways in which these areas are brought together. Specifically, the paper discusses the importance of comprehensive support for context awareness within data wrangling, and the need for adaptive, pay-as-you-go solutions that automatically tune the wrangling process to the requirements and resources of the specific application.

Technical developments, such as the web of data and web data extraction, combined with policy developments such as those relating to open government or open science, are leading to the availability of increasing numbers of data sources. Indeed, given these physical sources, it is then also possible to create further virtual sources that integrate, aggregate or summarise the data from the original sources. As a result, there is a plethora of data sources, from which a small subset may be able to provide the information required to support a task. The number and rate of change in the available sources is likely to make manual source selection and curation by experts impractical for many applications, leading to the need to pursue a pay-as-you-go approach, in which crowds or data consumers annotate results based on their correctness or suitability, with the resulting annotations used to inform, e.g., source selection algorithms. However, for pay-as-you-go feedback collection to be cost-effective, it may be necessary to select judiciously the data items on which feedback is to be obtained. This paper describes OLBP (Ordering and Labelling By Precision), a heuristics-based approach to the targeting of data items for feedback to support mapping and source selection tasks, where users express their preferences in terms of the trade-off between precision and recall. The proposed approach is then evaluated on two different scenarios, mapping selection with synthetic data, and source selection with real data produced by web data extraction. The results demonstrate a significant reduction in the amount of feedback required to reach user-provided objectives when using OLBP.

It has become widely recognized that user feedback can play a fundamental role in facilitating information integration tasks, e.g., the construction of integration schema and the specification of schema mappings. While promising, existing proposals make the assumption that the users providing feedback expect the same results from the integration system. In practice, however, different users may anticipate different results, due, e.g., to their preferences or application of interest, in which case the feedback they provide may be conflicting, thereby deteriorating the quality of the services provided by the integration system. In this paper, we present clustering strategies for grouping information integration users into groups of users with similar expectations as to the results delivered by the integration system. As well as grouping information integration users, we show that clustering results can be used as inputs to a wide range of functionalities that are relevant in the context of crowd-driven information integration. Specifically, we show that clustering can be used to identify feedback of relevance to a given user by exploiting the feedback provided by other users in the same cluster. We report on evaluation exercises that assess the effectiveness of the clustering strategies we propose, and showcase the benefits community- and crowd-driven information integration can derive from clustering.

Wireless sensor networks enable cost-effective data collection for tasks such as precision agriculture and environment monitoring. However, the resource-constrained nature of sensor nodes, which often have both limited computational capabilities and battery lifetimes, means that applications that use them must make judicious use of these resources. Research that seeks to support data intensive sensor applications has explored a range of approaches and developed many different techniques, including bespoke algorithms for specific analyses and generic sensor network query processors. However, all such proposals sit within a multi-dimensional design space, where it can be difficult to understand the implications of specific decisions and to identify optimal solutions. This paper presents a benchmark that seeks to support the systematic analysis and comparison of different techniques and platforms, enabling both development and user communities to make well informed choices. The contributions of the paper include: (i) the identification of key variables and performance metrics; (ii) the specification of experiments that explore how different types of task perform under different metrics for the controlled variables; and (iii) an application of the benchmark to investigate the behavior of several representative platforms and techniques.

Wireless sensor networks (WSN) are used by many applications for event and environmental monitoring. Due to the resource-limited nodes in WSNs, there has been much research into extending the functional lifetime of the network through energy-saving techniques. Sensor Network Query Processing (SNQP) is one such technique. SNQP uses information about a query and the WSN over which it is to be run, to generate an energy-efficient Query Execution Plan (QEP) that distributes processing in the form of QEP fragments to the nodes in the WSN. However, any QEP is likely to drain the batteries of the nodes unevenly, and, as a result, nodes used in a QEP may run out of energy when there are significant energy stocks still available in the WSN. An adaptive query processor could react to energy depletion, for example, by generating a revised plan that refrains from using the drained nodes. However, adapting only when a node has been depleted may provide few opportunities for the creation of effective new QEPs. In this paper, we introduce an approach that determines, at query compilation time, a sequence of QEPs with switch times for transitioning between successive plans with a view to extending the overall lifetime of the query. We describe how this approach has been implemented as an extension to an existing SNQP and present experimental results indicating that it can significantly increase QEP lifetimes.

Semantic annotation of web services has been proposed as a solution to the problem of discovering services to fit a particular need and reusing them appropriately. While there exist tools that assist human users in the annotation task, e.g., Radiant and Meteor-S, no semantic annotation proposal considers the problem of verifying the accuracy of the resulting annotations. Early evidence from workflow compatibility checking suggests that the proportion of annotations that contain some form of inaccuracy is high, and yet no tools exist to help annotators to test the results of their work systematically before they are deployed for public use. In this paper, we adapt techniques from conventional software testing to the verification of semantic annotations for web service input and output parameters. We present an algorithm for the testing process and discuss ways in which manual effort from the annotator during testing can be reduced. We also present two adequacy criteria for specifying test cases used as input for the testing process. These criteria are based on structural coverage of the domain ontology used for annotation. The results of an evaluation exercise, based on a collection of annotations for bioinformatics web services, show that defects can be successfully detected by the technique.

This work investigates a particular instance of the problem of designing efficient adaptive systems, under the condition that each adaptation decision incurs some nonnegligible cost when enacted. More specifically, we deal with the problem of dynamic, intraquery load balancing in parallel database queries across heterogeneous nodes in a way that takes into account the inherent cost of adaptations and thus avoids both overreacting and deciding when to adapt in a completely heuristic manner. The latter may lead to serious performance degradation in several cases, such as periodic and random imbalances. We follow a control theoretical approach to this problem; more specifically, we propose a multiple-input multiple-output feedback linear quadratic regulation (LQR) controller, which captures the tradeoff between reaching a balanced state and the cost inherent in such adaptations. Our approach, apart from benefitting from and being characterized by a solid theoretical foundation, exhibits better performance than state-of-the-art heuristics in realistic situations, as verified by thorough evaluation.

Sensor Networks have received considerable attention recently, as they provide manifold benefits. Not only are they a means for data acquisition and monitoring of unexplored or inaccessible areas, they are also a low-cost alternative for sensing the environment, which greatly aids to better understand our surroundings. A major motivation in either occasion is to acknowledge endangering situations and take action(s) accordingly. To this end, we would like to enable data mining or analysis techniques on top or, even better, within such networks, due to the prohibitive cost of communication in this setting. In this work, we demonstrate running data mining algorithms on a set of sensors, which are of low-processing power. In addition to showcasing the execution of data analysis algorithms on resource-constrained hardware, our demo is intended to show how to take advantage of the properties of each algorithm to make better use of the sensors and their capabilities. We support the execution and monitoring of these algorithms with a graphical user interface (GUI).

The vision of dataspaces proposes an alternative to classical data integration approaches with reduced up-front costs followed by incremental improvement on a pay-as-you-go basis. In this paper, we demonstrate DSToolkit, a system that allows users to provide feedback on results of queries posed over an integration schema. Such feedback is then used to annotate the mappings with their respective precision and recall. The system then allows a user to state the expected levels of precision (or recall) that the query results should exhibit and, in order to produce those results, the system selects those mappings that are predicted to meet the stated constraints.

Abstract Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has the responsibility of establishing how best to map tasks within a workflow to the available resources. As workflows are typically run over shared resources, and thus face unpredictable and changing resource capabilities, there may be benefit to be derived from adapting the task‐to‐resource mapping while a workflow is executing. This paper describes the use of utility functions to express the relative merits of alternative mappings; in essence, a utility function can be used to give a score to a candidate mapping, and the exploration of alternative mappings can be cast as an optimization problem. In this approach, changing the utility function allows adaptations to be carried out with a view to meeting different objectives. The contributions of this paper include: (i) a description of how adaptive workflow execution can be expressed as an optimization problem where the objective of the adaptation is to maximize a utility function; (ii) a description of how the approach has been applied to support adaptive workflow execution in execution environments consisting of multiple resources, such as grids or clouds, in which adaptations are coordinated across multiple workflows; and (iii) an experimental evaluation of the approach with utility measures based on response time and profit using the Pegasus workflow system. Copyright © 2010 John Wiley & Sons, Ltd.

The specification of schema mappings has proved to be time and resource consuming, and has been recognized as a critical bottleneck to the large scale deployment of data integration systems. In an attempt to address this issue, dataspaces have been proposed as a data management abstraction that aims to reduce the up-front cost required to setup a data integration system by gradually specifying schema mappings through interaction with end users in a pay-as-you-go fashion. As a step in this direction, we explore an approach for incrementally annotating schema mappings using feedback obtained from end users. In doing so, we do not expect users to examine mapping specifications; rather, they comment on results to queries evaluated using the mappings. Using annotations computed on the basis of user feedback, we present a method for selecting from the set of candidate mappings, those to be used for query evaluation considering user requirements in terms of precision and recall. In doing so, we cast mapping selection as an optimization problem. Mapping annotations may reveal that the quality of schema mappings is poor. We also show how feedback can be used to support the derivation of better quality mappings from existing mappings through refinement. An evolutionary algorithm is used to efficiently and effectively explore the large space of mappings that can be obtained through refinement. The results of evaluation exercises show the effectiveness of our solution for annotating, selecting and refining schema mappings.

In adaptive query processing, the way in which a query is evaluated is changed in the light of feedback obtained from the environment during query evaluation. Such feedback may, for example, establish that misleading selectivity estimates were used when the query was compiled, leading to the optimizer choosing an inappropriate join order or unsuitable join algorithms. This paper describes how joins can be reordered, and the join algorithms used replaced, while they are being evaluated in pipelined plans. Where joins are reordered and/or replaced during their evaluation, the approach avoids duplicating work that has already been carried out, by resuming from where the previous plan left off. The approach has been evaluated empirically, and shown to be effective for improving query performance in the light of misleading selectivity estimates.

The management and querying of workflow provenance data underpins a collection of activities, including the analysis of workflow results, and the debugging of workflows or services. Such activities require efficient evaluation of lineage queries over potentially complex and voluminous provenance logs. Näive implementations of lineage queries navigate provenance logs by joining tables that represent the flow of data between connected processors invoked from workflows. In this paper we provide an approach to provenance querying that: (i) avoids joins over provenance logs by using information about the workflow definition to inform the construction of queries that directly target relevant lineage results; (ii) provides fine grained provenance querying, even for workflows that create and consume collections; and (iii) scales effectively to address complex workflows, workflows with large intermediate data sets, and queries over multiple workflows.

MOTIVATION: Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations. RESULTS: We propose the Systems Biology Results Markup Language (SBRML), an XML-based language that associates a model with several datasets. Each dataset is represented as a series of values associated with model variables, and their corresponding parameter values. SBRML provides a flexible way of indexing the results to model parameter values, which supports both spreadsheet-like data and multidimensional data cubes. We present and discuss several examples of SBRML usage in applications such as enzyme kinetics, microarray gene expression and various types of simulation results. AVAILABILITY AND IMPLEMENTATION: The XML Schema file for SBRML is available at http://www.comp-sys-bio.org/SBRML under the Academic Free License (AFL) v3.0.

BACKGROUND: The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources. RESULTS: Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis. CONCLUSIONS: Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.

Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has responsibility for establishing how best to map tasks within a workflow to the available resources. As workflows are typically run over shared resources, and thus face unpredictable and changing resource capabilties, there may be benefit to be derived from adapting the task-to-resource mapping while a workflow is executing. This paper describes the use of utility functions to express the relative merits of alternative mappings; in essence, a utility function can be used to give a score to a candidate mapping, and the exploration of alternative mappings can be cast as an optimization problem. In this approach, changing the utility function allows adaptations to be carried out with a view to meeting different objectives. The contributions of this paper include: (i) a description of how adaptive workflow execution can be expressed as an optimization problem where the objective of the adaptation is to maximize some property expressed as a utility function; (ii) a description of how the approach has been applied to support adaptive workflow execution in grids; and (iii) an experimental evaluation of the resulting approach for alternative utility measures based on response time and profit.

MOTIVATION: Most experimental evidence on kinetic parameters is buried in the literature, whose manual searching is complex, time consuming and partial. These shortcomings become particularly acute in systems biology, where these parameters need to be integrated into detailed, genome-scale, metabolic models. These problems are addressed by KiPar, a dedicated information retrieval system designed to facilitate access to the literature relevant for kinetic modelling of a given metabolic pathway in yeast. Searching for kinetic data in the context of an individual pathway offers modularity as a way of tackling the complexity of developing a full metabolic model. It is also suitable for large-scale mining, since multiple reactions and their kinetic parameters can be specified in a single search request, rather than one reaction at a time, which is unsuitable given the size of genome-scale models. RESULTS: We developed an integrative approach, combining public data and software resources for the rapid development of large-scale text mining tools targeting complex biological information. The user supplies input in the form of identifiers used in relevant data resources to refer to the concepts of interest, e.g. EC numbers, GO and SBO identifiers. By doing so, the user is freed from providing any other knowledge or terminology concerned with these concepts and their relations, since they are retrieved from these and cross-referenced resources automatically. The terminology acquired is used to index the literature by mapping concepts to their synonyms, and then to textual documents mentioning them. The indexing results and the previously acquired knowledge about relations between concepts are used to formulate complex search queries aiming at documents relevant to the user's information needs. The conceptual approach is demonstrated in the implementation of KiPar. Evaluation reveals that KiPar performs better than a Boolean search. The precision achieved for abstracts (60%) and full-text articles (48%) is considerably better than the baseline precision (44% and 24%, respectively). The baseline recall is improved by 36% for abstracts and by 100% for full text. It appears that full-text articles are a much richer source of information on kinetic data than are their abstracts. Finally, the combined results for abstracts and full text compared with the curated literature provide high values for relative recall (88%) and novelty ratio (92%), suggesting that the system is able to retrieve a high proportion of new documents. AVAILABILITY: Source code and documentation are available at: (http://www.mcisb.org/resources/kipar/).

BACKGROUND: High content live cell imaging experiments are able to track the cellular localisation of labelled proteins in multiple live cells over a time course. Experiments using high content live cell imaging will generate multiple large datasets that are often stored in an ad-hoc manner. This hinders identification of previously gathered data that may be relevant to current analyses. Whilst solutions exist for managing image data, they are primarily concerned with storage and retrieval of the images themselves and not the data derived from the images. There is therefore a requirement for an information management solution that facilitates the indexing of experimental metadata and results of high content live cell imaging experiments. RESULTS: We have designed and implemented a data model and information management solution for the data gathered through high content live cell imaging experiments. Many of the experiments to be stored measure the translocation of fluorescently labelled proteins from cytoplasm to nucleus in individual cells. The functionality of this database has been enhanced by the addition of an algorithm that automatically annotates results of these experiments with the timings of translocations and periods of any oscillatory translocations as they are uploaded to the repository. Testing has shown the algorithm to perform well with a variety of previously unseen data. CONCLUSION: Our repository is a fully functional example of how high throughput imaging data may be effectively indexed and managed to address the requirements of end users. By implementing the automated analysis of experimental results, we have provided a clear impetus for individuals to ensure that their data forms part of that which is stored in the repository. Although focused on imaging, the solution provided is sufficiently generic to be applied to other functional proteomics and genomics experiments. The software is available from: fhttp://code.google.com/p/livecellim/

Abstract Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has the responsibility of establishing how best to execute a workflow given the available resources. The Pegasus workflow management system compiles abstract workflows into concrete execution plans, and has been widely used in large‐scale e‐Science applications. This paper describes an extension to Pegasus whereby resource allocation decisions are revised during workflow evaluation, in the light of feedback on the performance of jobs at runtime. The contributions of this paper include: (i) a description of how adaptive processing has been retrofitted to an existing workflow management system; (ii) a scheduling algorithm that allocates resources based on runtime performance; and (iii) an experimental evaluation of the resulting infrastructure using grid middleware over clusters. Copyright © 2009 John Wiley & Sons, Ltd.

We present a novel sensor network query processing architecture that (a) covers all the query optimization phases that are required to map a declarative query to executable code; and (b) does so for a more expressive query language than has heretofore been supported over sensor networks. The architecture is founded on the view that a sensor network truly is a distributed computing infrastructure, albeit a very constrained one. As such, we address the problem of how to develop a comprehensive optimizer for an expressive declarative continuous query language over acquisitional streams as one of finding extensions to a classical distributed query processing architecture that contend with the peculiarities of sensor networks as an environment for distributed computing.

Proteomics, the study of the protein complement of a biological system, is generating increasing quantities of data from rapidly developing technologies employed in a variety of different experimental workflows. Experimental processes, e.g. for comparative 2D gel studies or LC-MS/MS analyses of complex protein mixtures, involve a number of steps: from experimental design, through wet and dry lab operations, to publication of data in repositories and finally to data annotation and maintenance. The presence of inaccuracies throughout the processing pipeline, however, results in data that can be untrustworthy, thus offsetting the benefits of high-throughput technology. While researchers and practitioners are generally aware of some of the information quality issues associated with public proteomics data, there are few accepted criteria and guidelines for dealing with them. In this article, we highlight factors that impact on the quality of experimental data and review current approaches to information quality management in proteomics. Data quality issues are considered throughout the lifecycle of a proteomics experiment, from experiment design and technique selection, through data analysis, to archiving and sharing.

MOTIVATION: The Functional Genomics Experiment Object Model (FuGE) supports modelling of experimental processes either directly or through extensions that specialize FuGE for use in specific contexts. FuGE applications commonly include components that capture, store and search experiment descriptions, where the requirements of different applications have much in common. RESULTS: We describe a toolkit that supports data capture, storage and web-based search of FuGE experiment models; the toolkit can be used directly on FuGE compliant models or configured for use with FuGE extensions. The toolkit is illustrated using a FuGE extension standardized by the proteomics standards initiative, namely GelML. AVAILABILITY: The toolkit and a demonstration are available at http://code.google.com/p/fugetoolkit

BACKGROUND: The systematic capture of appropriately annotated experimental data is a prerequisite for most bioinformatics analyses. Data capture is required not only for submission of data to public repositories, but also to underpin integrated analysis, archiving, and sharing - both within laboratories and in collaborative projects. The widespread requirement to capture data means that data capture and annotation are taking place at many sites, but the small scale of the literature on tools, techniques and experiences suggests that there is work to be done to identify good practice and reduce duplication of effort. RESULTS: This paper reports on experience gained in the deployment of the Pedro data capture tool in a range of representative bioinformatics applications. The paper makes explicit the requirements that have recurred when capturing data in different contexts, indicates how these requirements are addressed in Pedro, and describes case studies that illustrate where the requirements have arisen in practice. CONCLUSION: Data capture is a fundamental activity for bioinformatics; all biological data resources build on some form of data capture activity, and many require a blend of import, analysis and annotation. Recurring requirements in data capture suggest that model-driven architectures can be used to construct data capture infrastructures that can be rapidly configured to meet the needs of individual use cases. We have described how one such model-driven infrastructure, namely Pedro, has been deployed in representative case studies, and discussed the extent to which the model-driven approach has been effective in practice.

BACKGROUND: Many bioinformatics applications rely on controlled vocabularies or ontologies to consistently interpret and seamlessly integrate information scattered across public resources. Experimental data sets from metabolomics studies need to be integrated with one another, but also with data produced by other types of omics studies in the spirit of systems biology, hence the pressing need for vocabularies and ontologies in metabolomics. However, it is time-consuming and non trivial to construct these resources manually. RESULTS: We describe a methodology for rapid development of controlled vocabularies, a study originally motivated by the needs for vocabularies describing metabolomics technologies. We present case studies involving two controlled vocabularies (for nuclear magnetic resonance spectroscopy and gas chromatography) whose development is currently underway as part of the Metabolomics Standards Initiative. The initial vocabularies were compiled manually, providing a total of 243 and 152 terms. A total of 5,699 and 2,612 new terms were acquired automatically from the literature. The analysis of the results showed that full-text articles (especially the Materials and Methods sections) are the major source of technology-specific terms as opposed to paper abstracts. CONCLUSIONS: We suggest a text mining method for efficient corpus-based term acquisition as a way of rapidly expanding a set of controlled vocabularies with the terms used in the scientific literature. We adopted an integrative approach, combining relatively generic software and data resources for time- and cost-effective development of a text mining tool for expansion of controlled vocabularies across various domains, as a practical alternative to both manual term collection and tailor-made named entity recognition methods.

Abstract Traditionally, database management systems have been associated with high‐cost, high‐quality functionalities. That is, powerful capabilities are provided, but only in response to careful design, procurement, deployment and administration. This has been successful in many contexts, but in an environment in which data are available in increasing quantities under the management of a growing collection of distributed applications, and where effective use of available data often provides a competitive edge, there is a requirement for various benefits of a comprehensive data management infrastructure to be made available with rather fewer of the costs. If this requirement is to be met, automation will need to be deployed much more widely and systematically in data management platforms. This paper reviews recent results on autonomic data management, makes a case that current practice presents significant opportunities for further development and argues that comprehensive support for automation should be central to future data management infrastructures, whether centralized or distributed. Copyright © 2008 John Wiley & Sons, Ltd.

Semantic annotations of web services can support the effective and efficient discovery of services, and guide their composition into workflows. At present, however, the practical utility of such annotations is limited by the small number of service annotations available for general use. Manual annotation of services is a time consuming and thus expensive task, so some means are required by which services can be automatically (or semi-automatically) annotated. In this paper, we show how information can be inferred about the semantics of operation parameters based on their connections to other (annotated) operation parameters within tried-and-tested workflows. Because the data links in the workflows do not necessarily contain every possible connection of compatible parameters, we can infer only constraints on the semantics of parameters. We show that despite their imprecise nature these so-called loose annotations are still of value in supporting the manual annotation task, inspecting workflows and discovering services. We also show that derived annotations for already annotated parameters are useful. By comparing existing and newly derived annotations of operation parameters, we can support the detection of errors in existing annotations, the ontology used for annotation and in workflows. The derivation mechanism has been implemented, and its practical applicability for inferring new annotations has been established through an experimental evaluation. The usefulness of the derived annotations is also demonstrated.

Grid computational resources, as well as being heterogeneous, may also exhibit unpredictable, volatile behaviour. Therefore, query processing on the Grid needs to be adaptive in order to cope with evolving resource characteristics, such as machine load and availability. To address this challenge in a Grid environment, the non-adaptive OGSA-DQP1 system described in [1] has been enhanced with adaptive capabilities.

BACKGROUND: The proliferation of data repositories in bioinformatics has resulted in the development of numerous interfaces that allow scientists to browse, search and analyse the data that they contain. Interfaces typically support repository access by means of web pages, but other means are also used, such as desktop applications and command line tools. Interfaces often duplicate functionality amongst each other, and this implies that associated development activities are repeated in different laboratories. Interfaces developed by public laboratories are often created with limited developer resources. In such environments, reducing the time spent on creating user interfaces allows for a better deployment of resources for specialised tasks, such as data integration or analysis. Laboratories maintaining data resources are challenged to reconcile requirements for software that is reliable, functional and flexible with limitations on software development resources. RESULTS: This paper proposes a model-driven approach for the partial generation of user interfaces for searching and browsing bioinformatics data repositories. Inspired by the Model Driven Architecture (MDA) of the Object Management Group (OMG), we have developed a system that generates interfaces designed for use with bioinformatics resources. This approach helps laboratory domain experts decrease the amount of time they have to spend dealing with the repetitive aspects of user interface development. As a result, the amount of time they can spend on gathering requirements and helping develop specialised features increases. The resulting system is known as Pierre, and has been validated through its application to use cases in the life sciences, including the PEDRoDB proteomics database and the e-Fungi data warehouse. CONCLUSION: MDAs focus on generating software from models that describe aspects of service capabilities, and can be applied to support rapid development of repository interfaces in bioinformatics. The Pierre MDA is capable of supporting common database access requirements with a variety of auto-generated interfaces and across a variety of repositories. With Pierre, four kinds of interfaces are generated: web, stand-alone application, text-menu, and command line. The kinds of repositories with which Pierre interfaces have been used are relational, XML and object databases.

Abstract Object database management systems (ODBMSs) are now established as the database management technology of choice for a range of challenging data intensive applications. Furthermore, the applications associated with object databases typically have stringent performance requirements, and some are associated with very large data sets. An important feature for the performance of object databases is the speed at which relationships can be explored. In queries, this depends on the effectiveness of different join algorithms into which queries that follow relationships can be compiled. This paper presents a performance evaluation of the Polar parallel object database system, focusing in particular on the performance of parallel join algorithms. Polar is a parallel, shared‐nothing implementation of the Object Database Management Group (ODMG) standard for object databases. The paper presents an empirical evaluation of queries expressed in the ODMG Query Language (OQL), as well as a cost model for the parallel algebra that is used to evaluate OQL queries. The cost model is validated against the empirical results for a collection of queries using four different join algorithms, one that is value based and three that are pointer based. Copyright © 2005 John Wiley & Sons, Ltd.

This article presents a complete spatio-temporal object DBMS called Tripod, in the context of an application to support the management of previously developed land (PDL). In particular the article focuses on: the techniques that are available to realise the application data model; the facilities necessary to realise the operational semantics of updates, and the facilities that are available to identify interesting patterns of spatio-temporal change. Whilst there exist other proposals for data models and query languages for spatio-temporal object databases, Tripod is unique in that it provides: a complete implementation of an expressive spatio-temporal data model to allow modelling of (a)spatial time-varying data; the first example of an implementation of language bindings to support manipulation of stored data; and the first complete implementation of a spatio-temporal OQL that we know of. The article also focuses on the benefits arising from use of a spatio-temporal DBMS on an important category of application, namely land parcel management.

BACKGROUND: Several data formats have been developed for large scale biological experiments, using a variety of methodologies. Most data formats contain a mechanism for allowing extensions to encode unanticipated data types. Extensions to data formats are important because the experimental methodologies tend to be fairly diverse and rapidly evolving, which hinders the creation of formats that will be stable over time. RESULTS: In this paper we review the data formats that exist in functional genomics, some of which have become de facto or de jure standards, with a particular focus on how each domain has been modelled, and how each format allows extensions. We describe the tasks that are frequently performed over data formats and analyse how well each task is supported by a particular modelling structure. CONCLUSION: From our analysis, we make recommendations as to the types of modelling structure that are most suitable for particular types of experimental annotation. There are several standards currently under development that we believe could benefit from systematically following a set of guidelines.

Abstract Initially, Grid technologies were principally associated with supercomputer centres and large‐scale scientific applications in physics and astronomy. They are now increasingly seen as being relevant to many areas of e‐Science and e‐Business. The emergence of the Open Grid Services Architecture (OGSA), to complement the ongoing activity on Web Services standards, promises to provide a service‐based platform that can meet the needs of both business and scientific applications. Early Grid applications focused principally on the storage, replication and movement of file‐based data. Now the need for the full integration of database technologies with Grid middleware is widely recognized. Not only do many Grid applications already use databases for managing metadata, but increasingly many are associated with large databases of domain‐specific information (e.g. biological or astronomical data). This paper describes the design and implementation of OGSA‐DAI, a service‐based architecture for database access over the Grid. The approach involves the design of Grid Data Services that allow consumers to discover the properties of structured data stores and to access their contents. The initial focus has been on support for access to Relational and XML data, but the overall architecture has been designed to be extensible to accommodate different storage paradigms. The paper describes and motivates the design decisions that have been taken, and illustrates how the approach supports a range of application scenarios. The OGSA‐DAI software is freely available from http://www.ogsadai.org.uk . Copyright © 2005 John Wiley & Sons, Ltd.

Abstract The U.K. e‐Science Programme is a £250 million, five‐year initiative which has funded over 100 projects. These application‐led projects are underpinned by an emerging set of core middleware services that allow the coordinated, collaborative use of distributed resources. This set of middleware services runs on top of the research network and beneath the applications we call the ‘Grid’. Grid middleware is currently in transition from pre‐Web Service versions to a new version based on Web Services. Unfortunately, only a very basic set of Web Services embodied in the Web Services Interoperability proposal, WS‐I, are agreed by most IT companies. IBM and others have submitted proposals for Web Services for Grids—the Web Services ResourceFramework and Web Services Notification specifications—to the OASIS organization for standardization. This process could take up to 12 months from March 2004 and the specifications are subject to debate and potentially significant changes. Since several significant U.K. e‐Science projects come to an end before the end of this process, the U.K. needs to develop a strategy that will protect the U.K.'s investment in Grid middleware by informing the Open Middleware Infrastructure Institute's (OMII) roadmap and U.K. middleware repository in Southampton. This paper sets out an evolutionary roadmap that will allow us to capture generic middleware components from projects in a form that will facilitate migration or interoperability with the emerging Grid Web Services standards and with ongoing OGSA developments. In this paper we therefore define a set of Web Services specifications, which we call ‘WS‐I+’ to reflect the fact that this is a larger set than currently accepted by WS‐I, that we believe will enable us to achieve the twin goals of capturing these components and facilitating migration to future standards. We believe that the extra Web Services specifications we have included in WS‐I+ are both helpful in building e‐Science Grids and likely to be widely accepted. Copyright © 2005 John Wiley & Sons, Ltd.

UNLABELLED: Pedro is a Java application that dynamically generates data entry forms for data models expressed in XML Schema, producing XML data files that validate against this schema. The software uses an intuitive tree-based navigation system, can supply context-sensitive help to users and features a sophisticated interface for populating data fields with terms from controlled vocabularies. The software also has the ability to import records from tab delimited text files and features various validation routines. AVAILABILITY: The application, source code, example models from several domains and tutorials can be downloaded from http://pedro.man.ac.uk/.

My Grid is an e-Science Grid project that aims to help biologists and bioinformaticians to perform workflow-based in silico experiments, and help them to automate the management of such workflows through personalisation, notification of change and publication of experiments. In this paper, we describe the architecture of my Grid and how it will be used by the scientist. We then show how my Grid can benefit from agents technologies. We have identified three key uses of agent technologies in my Grid: user agents, able to customize and personalise data, agent communication languages offering a generic and portable communication medium, and negotiation allowing multiple distributed entities to reach service level agreements.

This paper presents an approach to answering queries over an ontology modelled using a description logic. The ontology acts as a global schema, providing a declarative description of the concepts of the domain, the instances of which are stored in (potentially many) object-wrapped sources. Queries are expressed using terms from the rich vocabulary of the ontology, and are translated into an equivalent calculus expression, which references only the objects available in the source databases. The query is then optimized on the basis of information from the ontology and the source databases. Distinctive features of the approach include: the use of the expressive ALCQI description logic, which supports both ontology definition and query expression; the adoption of a global-as-view approach to relating the ontology to the sources; and the use of the ontology to direct semantic optimization of queries phrased over specific sources. The approach is being developed in, and is illustrated using examples from, bioinformatics.

Current research in the biosciences depends heavily on the effective exploitation of huge amounts of data. These are in disparate formats, remotely dispersed, and based on the different vocabularies of various disciplines. Furthermore, data are often stored or distributed using formats that leave implicit many important features relating to the structure and semantics of the data. Conceptual data modelling involves the development of implementation-independent models that capture and make explicit the principal structural properties of data. Entities such as a biopolymer or a reaction, and their relations, eg catalyses, can be formalised using a conceptual data model. Conceptual models are implementation-independent and can be transformed in systematic ways for implementation using different platforms, eg traditional database management systems. This paper describes the basics of the most widely used conceptual modelling notations, the ER (entity-relationship) model and the class diagrams of the UML (unified modelling language), and illustrates their use through several examples from bioinformatics. In particular, models are presented for protein structures and motifs, and for genomic sequences.

Effective analysis of genome sequences and associated functional data requires access to many different kinds of biological information. For example, when analysing gene expression data, it may be useful to have access to the sequences upstream of the genes, or to the cellular location of their protein products. Such information is currently stored in different formats at different sites in a way that does not readily allow integrated analyses. The Genome Information Management System (GIMS) is an object database that integrates genome sequence data with functional data on the transcriptome and on protein-protein interactions in a single data warehouse. We have used GIMS to store the Saccharomyces cerevisiae (yeast) genome and to demonstrate how the integrated storage of diverse kinds of genomic data can be beneficial for analysing data using context-rich queries and analyses. GIMS allows data to be stored in a way that reflects the underlying mechanisms in the organism, and permits complex questions to be asked of the data. This paper provides an overview of the GIMS system and describes some analyses that illustrate its use for analysing functional data sets for S. cerevisiae.

The development of any comprehensive proposal for spatio-temporal databases involves significant extensions to many aspects of a non-spatio-temporal architecture. One aspect that has received less attention than most is the development of a query calculus that can be used to provide a semantics for spatio-temporal queries and underpin an effective query optimization and evaluation framework. We show how a query calculus for spatio-temporal object databases that builds upon the monoid calculus proposed by Fegaras and Maier (2000) for ODMG-compliant database systems can be developed. The paper shows how an extension of the ODMG type system with spatial and temporal types can be accommodated into the monoid approach. It uses several queries over historical (possibly spatial) data to illustrate how, by mapping them into monoid comprehensions, the way is open for the application of a logical optimizer based on the normalization algorithm proposed by Fegaras and Maier.

Model-based user interface development environments show promise for improving the productivity of user interface developers, and possibly for improving the quality of developed interfaces. While model-based techniques have previously been applied to the area of database interfaces, they have not been specifically targeted at the important area of object database applications. Such applications make use of models that are semantically richer than their relational counterparts in terms of both data structures and application functionality. In general, model-based techniques have not addressed how the information referenced in such applications is manifested within the described models, and is utilised within the generated interface itself. This lack of experience with such systems has led to many model-based projects providing minimal support for certain features that are essential to such data intensive applications, and has prevented object database interface developers in particular from benefiting from model-based techniques. This paper presents the Teallach model-based user interface development environment for object databases, describing the models it supports, the relationships between these models, the tool used to construct interfaces using the models and the generation of Java programs from the declarative models. Distinctive features of Teallach include comprehensive facilities for linking models, a flexible development method, an open architecture, and the generation of running applications based on the models constructed by designers. © 2001 Elsevier Science B.V. All rights reserved.

Declarative models play an important role in most software design activities, by allowing designs to be constructed that selectively abstract over complex implementation details. In the user interface setting, Model-Based User Interface Development Environments (MB-UIDEs) provide a context within which declarative models can be constructed and related, as part of the interface design process. However, such declarative models are not usually directly executable, and may be difficult to relate to existing software components. It is therefore important that MB-UIDEs both fit in well with existing software architectures and standards, and provide an effective route from declarative interface specification to running user interfaces. This paper describes how user interface software is generated from declarative descriptions in the Teallach MB-UIDE. Distinctive features of Teallach include its open architecture, which connects directly to existing applications and widget sets, and the generation of executable interface applications in Java. This paper focuses on how Java programs, organized using the model-view-controller pattern (MVC), are generated from the task, domain and presentation models of Teallach.

Article Polar: an architecture for a parallel ODMG compliant object database Share on Authors: Jim Smith Department of Computing Science, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU UK Department of Computing Science, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU UKView Profile , Paul Watson Department of Computing Science, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU UK Department of Computing Science, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU UKView Profile , Sandra de F. Mendes Sampaio Department of Computing Science, University of Manchester, Oxford Road, Manchester, M13 9PL UK Department of Computing Science, University of Manchester, Oxford Road, Manchester, M13 9PL UKView Profile , Norman Paton Department of Computing Science, University of Manchester, Oxford Road, Manchester, M13 9PL UK Department of Computing Science, University of Manchester, Oxford Road, Manchester, M13 9PL UKView Profile Authors Info & Claims CIKM '00: Proceedings of the ninth international conference on Information and knowledge managementNovember 2000 Pages 352–359https://doi.org/10.1145/354756.354840Online:06 November 2000Publication History 12citation401DownloadsMetricsTotal Citations12Total Downloads401Last 12 Months3Last 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The importance of materialized views has grown signi cantly with the advent of data warehousing and OLAP technology.This increases the relevance of solutions to the problem of incrementally maintaining materialized views.So far, most w orkon this problem has been con ned to relational settings.Proposals that apply to object databases have either used non-standard models or fallen short of providing a comprehensive framework.This paper contributes a solution to the incremental view maintenance problem for a large class of views expressed in OQL, the query language of the ODMG standard for object databases.The solution applies to immediate update propagation, and works for any update operation on views de ned over a substantial subset of ODMG types.The approach presen ted has been fully implemented and preliminary performance results are reported.

Abstract Motivation: Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. Results: This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms. Availability: Most of the information models presented here have been implemented by the authors using an object database. The implementation of a public interface to this database is in progress. We hope to have a public release in the autumn of 2000, available from http://img.cs.man.ac.uk/gims. Contact: [email protected]

The TAMBIS project aims to provide transparent access to disparate biological databases and analysis tools, enabling users to utilize a wide range of resources with the minimum of effort. A prototype system has been developed that includes a knowledge base of biological terminology (the biological Concept Model), a model of the underlying data sources (the Source Model) and a 'knowledge-driven' user interface. Biological concepts are captured in the knowledge base using a description logic called GRAIL. The Concept Model provides the user with the concepts necessary to construct a wide range of multiple-source queries, and the user interface provides a flexible means of constructing and manipulating those queries. The Source Model provides a description of the underlying sources and mappings between terms used in the sources and terms in the biological Concept Model. The Concept Model and Source Model provide a level of indirection that shields the user from source details, providing a high level of source transparency. Source independent, declarative queries formed from terms in the Concept Model are transformed into a set of source dependent, executable procedures. Query formulation, translation and execution is demonstrated using a working example.

Conducting bioinformatic analyses involves biologists in expressing requests over a range of highly heterogeneous information sources and software tools. Such activities are laborious, and require detailed knowledge of the data structures and call interfaces of the different sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. In TAMBIS, queries are expressed in terms of an ontology implemented using a description logic, and queries over the ontology are rewritten to a middleware level for execution over the diverse sources. The paper describes query processing in TAMBIS, focusing in particular on the way source-independent concepts in the ontology are related to source-dependent middleware calls, and describing how the planner identifies efficient ways of evaluating user queries.

Conducting bioinformatic analyses involves biologists in expressing requests over a range of heterogeneous information sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. TAMBIS is available at.

MOTIVATION: An ontology of biological terminology provides a model of biological concepts that can be used to form a semantic framework for many data storage, retrieval and analysis tasks. Such a semantic framework could be used to underpin a range of important bioinformatics tasks, such as the querying of heterogeneous bioinformatics sources or the systematic annotation of experimental results. RESULTS: This paper provides an overview of an ontology [the Transparent Access to Multiple Biological Information Sources (TAMBIS) ontology or TaO] that describes a wide range of bioinformatics concepts. The present paper describes the mechanisms used for delivering the ontology and discusses the ontology's design and organization, which are crucial for maintaining the coherence of a large collection of concepts and their relationships. AVAILABILITY: The TAMBIS system, which uses a subset of the TaO described here, is accessible over the Web via http://img.cs.man.ac.uk/tambis (although in the first instance, we will use a password mechanism to limit the load on our server). The complete model is also available on the Web at the above URL.

In this paper we describe Kaleidoquery, a visual query language for object databases with the same expressive power as OQL. We will describe the design philosophy behind the filter flow nature of Kaleidoquery and present each of the language's constructs, giving examples and relating them to OQL. The Kaleidoquery language is described independent of any implementation details, but a brief description of a 3D interface currently under construction for Kaleidoquery is presented. The queries in this implementation of the language are translated into OQL and then passed to the object database O2 for evaluation.

ROCK & ROLL is a deductive object oriented database system that supports two languages, one imperative and the other deductive, both derived from the same object oriented data model. As the languages share a common type system, they can be integrated without manifesting impedance mismatches, and thus programmers can conveniently exploit both deductive and imperative features in a single application. The components of ROCK & ROLL are as follows: data model OM-OM supports a range of conventional modelling constructs, such as sets, sequences, aggregations and (both single and multiple) inheritance; deductive language ROLL-ROLL is a conventional first order deductive database language, which differs from Datalog with negation in being strictly typed (through type inference), having a structured clause base that associates rules with classes, and in that the extensional database is that of OM, rather than the relational model; and imperative language ROCK-ROCK is a conventional imperative object oriented programming language, with facilities for creating and manipulating OM objects, iteration, I/O, etc. The use of two languages has allowed us to keep the logic language ROLL simple, as facilities such as updates are not handled within ROLL, but rather in the closely integrated imperative language ROCK. The basic ROCK & ROLL system provides comprehensive modelling and programming facilities, but recent work has extended it with both active rules and spatial data types.

This paper presents a framework for the formal specification of active database systems, and shows how the framework can be used to describe the functionality of three well known example systems, namely Starburst, POSTGRES and Ariel. The framework has been developed using Object-Z to structure specifications in a way that emphasises commonalities and key differences between the designs, and that is readily extensible to support new constructs and systems. Such a formal framework can be used to provide formal descriptions of systems that have previously been described only informally, to compare the functionalities of different systems by contrasting support for fundamental concepts, and as a basis for reasoning about rule bases in the context of different active rule systems. The paper also demonstrates the applicability of object-oriented formal methods to the specification of advanced database functionality.

The object-oriented paradigm has a number of widely recognised strengths when applied to data management, but the increased complexity of actual systems compared with their relational predecessors often means that such databases are less readily accessible to nonprogrammers than relational systems. A number of proposals have been made for textual, form-based and graph-based query interfaces to object-oriented databases, but it is clear that a single approach cannot be considered to be the best, given the wide range of potential user groups, application domains and tasks. The paper presents a query interface to an object-oriented database which supports alternative user-level query paradigms in a fully integrated environment, thereby enabling different categories of user to select a preferred interface paradigm from a list of options. Furthermore, the interface enables users to examine queries written in one query interface using any of the other interface paradigms, which is useful for sharing queries in the multi-paradigm context, and for helping users familiar with one approach to learn another. The system has been prototyped using the ADAM object-oriented database system, and an experimental comparison of different interaction modes has been conducted.

In this paper a query optimiser for use with functional data model databases is described. The system is in use with a large database of protein structures from which examples are taken. The optimiser, which evaluates alternative paths through the object base, is written in Prolog and integrated with a parser for DAPLEX. The constructs of DAPLEX are easily expressed in Prolog, which has also proved suitable for implementing both the parser and rewrite rules. The implementation of the optimiser using rewrite rules is both concise and extensible.