Summary Reconciliation is the process of providing a consistent view of the data imported from different sources. Despite some efforts reported in the literature for providing data reconciliation solutions with asynchronous collaboration, the challenge of reconciling data when multiple users work asynchronously over local copies of the same imported data has received less attention. In this paper, we propose AcCORD, an asynchronous collaborative data reconciliation model based on data provenance. AcCORD is innovative because it supports applications in which all users are required to agree on the data values to provide a single consistent view to all of them, as well as applications that allow users to disagree on the data values to keep in their local copies but promote collaboration by sharing integration decisions. We also introduce a decision integration propagation method that keeps users from taking inconsistent decisions over data items present in several sources. Further, different policies based on data provenance are proposed for solving conflicts among multiusers' integration decisions. Our experimental analysis shows that AcCORD is efficient and effective. It performs well, and the results highlight its flexibility by generating either a single integrated view or different local views. We have also conducted interviews with end users to analyze the proposed policies and feasibility of the multiuser reconciliation. They provide insights with respect to acceptability, consistency, correctness, time‐saving, and satisfaction. Copyright © 2017 John Wiley & Sons, Ltd.

RDF datasets have increased rapidly over the last few years. In order to process SPARQL queries on these large datasets, much effort has been spent on developing horizontally scalable techniques, which involve data partitioning and parallel query processing. While distribution may provide storage scalability, it may also incur high communication costs for processing queries. In this paper, we present a parallel and distributed query rocessing approach that explores the existence of data allocation patterns, provided by a controlled data distribution, that determine how RDF triples should be grouped and stored on the same server. Fragments of the RDF datastore follow a given allocation pattern and correspond also to units of communication among servers. Based on this distribution model, we define two communication strategies for query processing: get-frag, which requests remote servers to send fragments that contain data required by a query, and send-result, which forwards intermediate results. These strategies are combined on a method, called 2ways, that chooses the adequate communication strategy whenever queries traverse fragment boundaries. We provide a cost function used to determine this choice and present experimental results. They show that our proposed technique effectively reduces the communication cost and improves the response time for processing SPARQL queries on a distributed RDF datastore.

The sensing of urban environments usually takes into account the deployment of a large number of devices to measure their environmental attributes, such as temperature, pressure, humidity, luminosity and pollution. In such applications, nearby sensors usually produce similar readings due to their spatial and temporal correlation. In the era of big data, management of collected data requires autonomous and scalable Wireless Sensor Network (WSN) structures. In this paper, we propose an in-network data storage model, called AQPM, that provides efficient processing of both spatial and value-based queries. AQPM is autonomous and scalable. That is, it does not rely on any central entity for neither managing data storage on sensor devices nor for processing queries. Scalability is achieved by grouping sensors with similar readings into clusters, while efficient query processing relies on the concept of repositories. Repositories are sensors that store readings of a set of clusters, and are the only ones that have to be contacted for answering queries. AQPM has been implemented on NS2 simulator and experimental results show that it is more effective than existing approaches.

Policies are used for developing adaptable and flexible systems in a variety of areas. They are especially suitable for reducing the complexity of managing tasks, by providing a mechanism for automatically tuning the system without human intervention. Policy-based systems have been applied for wireless sensor networks (WSNs) for controlling several functionalities. However, none of them has been proposed as a storage model, by making a clear distinction between storage functions and their behaviour. In this paper we propose SeSP, a Sensor Storage model based on Policies. SeSP explores concepts that are common in storage models proposed for WSNs in order to reduce the number of message transmissions and thus minimize the sensors' energy consumption. We have conducted a case study applying our policy-based system on two existing storage models: Scoop and DYSTO. Our experimental study, based on simulations, shows that SeSP can effectively reduce the number of transmissions, compared to the fixed values considered by both systems.

Applications for Wireless sensor networks (WSN) usually take into consideration the specificity of the environment in which they are deployed in order to save the sensors' limited resources. In particular, the sensing task in urban environments requires hundreds and even thousands of sensors to be spread over the monitored area. Moreover, in environmental monitoring applications, sensors that are closely located usually provide similar readings. That is, spatial proximity is related to data similarity. In this paper we propose SIDS (Spatial Indexing Based on Data Similarity for Sensor Networks), a data model that explores this characteristic in order to provide scalability and efficient query processing on urban WSNs. Scalability is achieved by grouping sensors with similar readings, while efficiency for processing queries relies on two strategies: the concept of repositories, which consist of sensors that act as datacenters, and an indexing structure designed for speeding up both spatial and value-based queries. We have implemented the proposed model and results from simulations on a variety of scenarios show that SIDS provides scalability and it outperforms CAG and Peer-tree, which are models that have been proposed for processing data and spatial queries, respectively.

This paper describes the design and architecture of a cloud-based relational database system. The system's core component is a storage engine, which is responsible for mapping the logical schema, based on relations, to a physical storage, based on a distributed key-value data store. The proposed stratified architecture provides physical data independence, by allowing different approaches for data mapping and partitioning, while the distributed data store is responsible for providing scalability, availability, data replication and ACID properties. A prototype of the system, named Phoenix, has been developed based on the proposed architecture using a transactional key-value store. Experimental studies on a cluster of commodity servers show that Phoenix preserves the desired properties of key-value stores, while providing relational database functionality at a very low overhead.

In some integration applications, users are allowed to import data from heterogeneous sources, but are not allowed to update source data directly. Imported data may be inconsistent, and even when inconsistencies are detected and solved, these changes may not be propagated to the sources due to their update policies. Therefore, they continue to provide the same inconsistent data in the future until the proper authority updates them. In this paper, we propose PrInt, a model that supports user's decisions on cleaning data to be automatically reapplied in subsequent integration processes. By reproducing previous decisions, the user may focus only on new inconsistencies originated from source modified data. The reproducibility provided by PrInt is based on logging, and by incorporating data provenance in the integration process.

Workflow systems have become increasingly popular for managing experiments where many bioinformatics tasks are chained together. Due to the large amount of data generated by these experiments and the need for reproducible results, provenance has become of paramount importance. Workflow systems are therefore starting to provide support for querying provenance. However, the amount of provenance information may be overwhelming, so there is a need for abstraction mechanisms to help users focus on the most relevant information. The technique we pursue is that of "user views". Since bioinformatics tasks may themselves be complex sub-workflows, a user view determines what level of sub-workflow the user can see, and thus what data and tasks are visible in provenance queries. In this paper, we formalize the notion of user views, demonstrate how they can be used in provenance queries, and give an algorithm for generating a user view based on which tasks are relevant for the user. We then describe our prototype and give performance results. Although presented in the context of scientific workflows, the technique applies to other data-oriented workflows.

Network monitoring is a complex task that generally requires the use of different tools for specific purposes. This paper describes a flexible network monitoring tool, called PaQueT, designed to meet a wide range of monitoring needs. The user can define metrics as queries in a process similar to writing queries on a database management system. This approach provides an easy mechanism to adapt the tool as system requirements evolve. PaQueT allows one to monitor values ranging from packet level metrics to those usually provided only by tools based on Netflow or SNMP. PaQueT has been developed as an extension of Borealis Data Stream Management System. The first advantage of our approach is the ability to generate measurements in real time, minimizing the volume of data stored; second, the tool can be easily extended to consider several types of network protocols. We have conducted an experimental study to verify the effectiveness of our approach, and to determine its capacity to process large volumes of data.

We present a technique for refining the design of relational storage for XML data based on XML key propagation. Three algorithms are presented: one checks whether a given functional dependency is propagated from XML keys via a predefined view; the others compute a minimum cover for all functional dependencies on a universal relation given XML keys. Experimental results show that these algorithms are efficient in practice. We also investigate the complexity of propagating other XML constraints to relations, and the effect of increasing the power of the transformation language. Computing XML key propagation is a first step toward establishing a connection between XML data and its relational representation at the semantic level.

We discuss the denition of keys for XML documents, paying particular attention to the concept of a relative key, which is commonly used in hierarchically structured documents and scientic databases.

Article Reasoning about nested functional dependencies Share on Authors: Carmem S. Hara Dept. of Computer and Information Science, University of Pennsylvania, Philadelphia, PA Dept. of Computer and Information Science, University of Pennsylvania, Philadelphia, PAView Profile , Susan B. Davidson Dept. of Computer and Information Science, University of Pennsylvania, Philadelphia, PA Dept. of Computer and Information Science, University of Pennsylvania, Philadelphia, PAView Profile Authors Info & Claims PODS '99: Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systemsMay 1999 Pages 91–100https://doi.org/10.1145/303976.303985Online:01 May 1999Publication History 35citation358DownloadsMetricsTotal Citations35Total Downloads358Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access