Interest-based RDF Update Propagation

Many LOD datasets, such as DBpedia and LinkedGeoData, are voluminous and process large amounts of requests from diverse applications. Many data products and services rely on full or partial local LOD replications to ensure faster querying and processing. While such replicas enhance the flexibility of information sharing and integration infrastructures, they also introduce data duplication with all the associated undesirable consequences. Given the evolving nature of the original and authoritative datasets, to ensure consistent and up-to-date replicas frequent replacements are required at a great cost. In this paper, we introduce an approach for interest-based RDF update propagation, which propagates only interesting parts of updates from the source to the target dataset. Effectively, this enables remote applications to `subscribe' to relevant datasets and consistently reflect the necessary changes locally without the need to frequently replace the entire dataset (or a relevant subset). Our approach is based on a formal definition for graph-pattern-based interest expressions that is used to filter interesting parts of updates from the source. We implement the approach in the iRap framework and perform a comprehensive evaluation based on DBpedia Live updates, to confirm the validity and value of our approach.Comment: 16 pages, Keywords: Change Propagation, Dataset Dynamics, Linked Data, Replicatio

Failure-awareness and dynamic adaptation in data scheduling

Over the years, scientific applications have become more complex and more data intensive. Especially large scale simulations and scientific experiments in areas such as physics, biology, astronomy and earth sciences demand highly distributed resources to satisfy excessive computational requirements. Increasing data requirements and the distributed nature of the resources made I/O the major bottleneck for end-to-end application performance. Existing systems fail to address issues such as reliability, scalability, and efficiency in dealing with wide area data access, retrieval and processing. In this study, we explore data-intensive distributed computing and study challenges in data placement in distributed environments. After analyzing different application scenarios, we develop new data scheduling methodologies and the key attributes for reliability, adaptability and performance optimization of distributed data placement tasks. Inspired by techniques used in microprocessor and operating system architectures, we extend and adapt some of the known low-level data handling and optimization techniques to distributed computing. Two major contributions of this work include (i) a failure-aware data placement paradigm for increased fault-tolerance, and (ii) adaptive scheduling of data placement tasks for improved end-to-end performance. The failure-aware data placement includes early error detection, error classification, and use of this information in scheduling decisions for the prevention of and recovery from possible future errors. The adaptive scheduling approach includes dynamically tuning data transfer parameters over wide area networks for efficient utilization of available network capacity and optimized end-to-end data transfer performance

OCTP. An optimistic transactional cache protocol with low abort rates

Since the early nineties transactional cache protocols have been intensively studied in the context of client-server database systems. Research has developed a variety of protocols and compared different aspects of their quality. In this paper, we present a new transactional cache protocol, called "Optimistic Caching Timestamp Protocol" (OCTP). OCTP is a pure optimistic protocol and represents a strong improvement over OCC - a classical optimistic transactional cache protocol. OCC is known to have very low message overhead but suffers from high transaction abort rates. In contrast, OCTP\u27s message overhead is the same as that of OCC but its abort rates are considerably lower. OCTP does not require locks to coordinate concurrent transactions but uses a backward validating timestamp-based approach instead. As opposed to all other known transactional cache protocols, it can allow transactions to commit which have read stale cached data elements while still asserting serializability. Its computational complexity is moderate and in particular, it does apply a potentially costly serializability graph test. We also present an extension of OCTP called "Semi-Optimistic Caching Timestamp Procotol" (SOCTP), which reduces abort rates further. In certain cases, SOCTP efficiently uses locks to prevent transaction aborts. This paper explains the concepts behind OCTP and proves its correctness using a multiversion transaction formalism. It sketches an efficient implementation of OCTP and compares OCTP as well as SOCTP against two leading conventional protocols, namely OCC and CBR. Simulation experiments show that both SOCTP and OCTP outperform OCC and CBR given that the network represents the bottleneck of a related database system

Maintaining consistency in client-server database systems with client-side caching

PhD ThesisCaching has been used in client-server database systems to improve the performance of applications. Much of the current work has concentrated on caching techniques at the server side, since the underlying assumption has been that clients are “thin” with application level processing taking place mainly at the server side. There are also a new class of “thick client” applications where clients need to access the database at the server but also perform substantial amount of processing at the client side; here client-side caching is needed to provide good performance for applications. This thesis presents a transactional cache consistency scheme suitable for systems with client-side caching. The scheme is based on the optimistic approach to concurrency control. The scheme provides serializability for committed transactions. This is in contrast to many modern systems that only provide the snapshot isolation property which is weaker than serializability. A novel feature is that the processing load for validating transactions at commit time is shared between clients and the database server, thereby reducing the load at the server. Read-only transactions can be validated at the client-side, without communicating with the server. Another feature is that the scheme permits disconnected operation, allowing clients with cached objects to work offline. The performance of the scheme is evaluated using simulation experiments. The experiments demonstrate that for mostly read only transaction load – for which caching is most effective - the scheme outperforms the existing concurrency control scheme with client-side caching considered to be the best, and matches the performance of the widely used scheme that only provides snapshot isolation. The results also show that the scheme in a disconnected environment provides reasonable performance.Directorate General of Higher Education, Ministry of National Education, Indonesia

Exploiting method semantics in client cache consistency protocols for object-oriented databases

PhD ThesisData-shipping systems are commonly used in client-server object-oriented databases. This is in- tended to utilise clients' resources and improve scalability by allowing clients to run transactions locally after fetching the required database items from the database server. A consequence of this is that a database item can be cached at more than one client. This therefore raises issues regarding client cache consistency and concurrency control. A number of client cache consistency protocols have been studied, and some approaches to concurrency control for object-oriented datahases have been proposed. Existing client consistency protocols, however, do not consider method semantics in concurrency control. This study proposes a client cache consistency protocol where method se- mantic can be exploited in concurrency control. It identifies issues regarding the use of method semantics for the protocol and investigates the performance using simulation. The performance re- sults show that this can result in performance gains when compared to existing protocols. The study also shows the potential benefits of asynchronous version of the protoco

An Adaptive Data-Shipping Architecture for Client Caching Data Management Systems

Data-shipping is an important form of data distribution architecture where data objects are retrieved from the server, and are cached and operated upon at the client nodes. This architecture reduces network latency and increases resource utilization at the client. Object database management systems (ODBMS), file-systems, mobile data management systems, multi-tiered Web-server systems and hybrid query-shipping/data-shipping architectures all use some variant of the data-shipping. Despite a decade of research, there is still a lack of consensus amongst the proponents of ODBMSs as to the type of data shipping architectures and algorithms that should be used. The absence of both robust (with respect to performance) algorithms, and a comprehensive performance study comparing the competing algorithms are the key reasons for this lack of agreement. In this paper we address both of these problems. We first present an adaptive data-shipping architecture which utilizes adaptive data transfer, cache consistency and recovery algorithms to improve the robustness (with respect to performance) of a data-shipping ODBMS. We then present a comprehensive performance study which evaluates the competing client-server architectures and algorithms. The study verifies the robustness of the new adaptive data-shipping architecture, provides new insights into the performance of the different competing algorithms, and helps to overturn some existing notions about some of the algorithms

Federated Query Processing over Heterogeneous Data Sources in a Semantic Data Lake

Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for citizens. Big Data plays an important role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Open data initiatives have encouraged the publication of Big Data by exploiting the decentralized nature of the Web, allowing for the availability of heterogeneous data generated and maintained by autonomous data providers. Consequently, the growing volume of data consumed by different applications raise the need for effective data integration approaches able to process a large volume of data that is represented in different format, schema and model, which may also include sensitive data, e.g., financial transactions, medical procedures, or personal data. Data Lakes are composed of heterogeneous data sources in their original format, that reduce the overhead of materialized data integration. Query processing over Data Lakes require the semantic description of data collected from heterogeneous data sources. A Data Lake with such semantic annotations is referred to as a Semantic Data Lake. Transforming Big Data into actionable knowledge demands novel and scalable techniques for enabling not only Big Data ingestion and curation to the Semantic Data Lake, but also for efficient large-scale semantic data integration, exploration, and discovery. Federated query processing techniques utilize source descriptions to find relevant data sources and find efficient execution plan that minimize the total execution time and maximize the completeness of answers. Existing federated query processing engines employ a coarse-grained description model where the semantics encoded in data sources are ignored. Such descriptions may lead to the erroneous selection of data sources for a query and unnecessary retrieval of data, affecting thus the performance of query processing engine. In this thesis, we address the problem of federated query processing against heterogeneous data sources in a Semantic Data Lake. First, we tackle the challenge of knowledge representation and propose a novel source description model, RDF Molecule Templates, that describe knowledge available in a Semantic Data Lake. RDF Molecule Templates (RDF-MTs) describes data sources in terms of an abstract description of entities belonging to the same semantic concept. Then, we propose a technique for data source selection and query decomposition, the MULDER approach, and query planning and optimization techniques, Ontario, that exploit the characteristics of heterogeneous data sources described using RDF-MTs and provide a uniform access to heterogeneous data sources. We then address the challenge of enforcing privacy and access control requirements imposed by data providers. We introduce a privacy-aware federated query technique, BOUNCER, able to enforce privacy and access control regulations during query processing over data sources in a Semantic Data Lake. In particular, BOUNCER exploits RDF-MTs based source descriptions in order to express privacy and access control policies as well as their automatic enforcement during source selection, query decomposition, and planning. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over data sources that not only contain the relevant entities to answer a query, but also are regulated by policies that allow for accessing these relevant entities. Finally, we tackle the problem of interest based update propagation and co-evolution of data sources. We present a novel approach for interest-based RDF update propagation that consistently maintains a full or partial replication of large datasets and deal with co-evolution