Toward a Unified Timestamp with explicit precision

Demographic and health surveillance (DS) systems monitor and document individual- and group-level processes in well-defined populations over long periods of time. The resulting data are complex and inherently temporal. Established methods of storing and manipulating temporal data are unable to adequately address the challenges posed by these data. Building on existing standards, a temporal framework and notation are presented that are able to faithfully record all of the time-related information (or partial lack thereof) produced by surveillance systems. The Unified Timestamp isolates all of the inherent complexity of temporal data into a single data type and provides the foundation on which a Unified Timestamp class can be built. The Unified Timestamp accommodates both point- and interval-based time measures with arbitrary precision, including temporal sets. Arbitrary granularities and calendars are supported, and the Unified Timestamp is hierarchically organized, allowing it to represent an unlimited array of temporal entities.demographic surveillance, standardization, temporal databases, temporal integrity, timestamp, valid time

Optimistic replication

Data replication is a key technology in distributed data sharing systems, enabling higher availability and performance. This paper surveys optimistic replication algorithms that allow replica contents to diverge in the short term, in order to support concurrent work practices and to tolerate failures in low-quality communication links. The importance of such techniques is increasing as collaboration through wide-area and mobile networks becomes popular. Optimistic replication techniques are different from traditional “pessimistic ” ones. Instead of synchronous replica coordination, an optimistic algorithm propagates changes in the background, discovers conflicts after they happen and reaches agreement on the final contents incrementally. We explore the solution space for optimistic replication algorithms. This paper identifies key challenges facing optimistic replication systems — ordering operations, detecting and resolving conflicts, propagating changes efficiently, and bounding replica divergence — and provides a comprehensive survey of techniques developed for addressing these challenges

On the support of versioning in distributed key-value stores

The ability to access and query data stored in multiple versions is an important asset for many applications, such as Web graph analysis, collaborative editing platforms, data forensics, or correlation mining. The storage and retrieval of versioned data requires a specific API and support from the storage layer. The choice of the data structures used to maintain versioned data has a fundamental impact on the performance of insertions and queries. The appropriate data structure also depends on the nature of the versioned data and the nature of the access patterns. In this paper we study the design and implementation space for providing versioning support on top of a distributed key-value store (KVS). We define an API for versioned data access supporting multiple writers and show that a plain KVS does not offer the necessary synchronization power for implementing this API. We leverage the support for listeners at the KVS level and propose a general construction for implementing arbitrary types of data structures for storing and querying versioned data. We explore the design space of versioned data storage ranging from a flat data structure to a distributed sharded index. The resulting system, \system, is implemented on top of an industrial-grade open-source KVS, Infinispan. Our evaluation, based on real-world Wikipedia access logs, studies the performance of each versioning mechanisms in terms of load balancing, latency and storage overhead in the context of different access scenarios

Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie

Virtual time synchronization in distributed database systems

Distributed systems synchronized by Virtual Time have been topics of recent interest. Virtual Time follows an optimistic philosophy relying on rollback for synchronization instead of abortion or blocKing Although many applications have been suggested as candidates for Virtual Time, few were simulated or implemented. This research reports on the first implementation and results of a Distributed Database Management System synchronized by virtual time. We argue that virtual time is a viable alternate concurrency control method for distributed database systems if its memory overhead can be absorbed