Checkpoint of an in-memory database is the main source of a persistent database image surviving a software crash, or a power outage, and is, together with transactions logs, a foundation for transaction durability. Since checkpoints are created simultaneously with transaction processing, they tend to decrease database throughput and increase its memory footprint. Of the current methods, most efficient are the fuzzy checkpoint algorithms that write dirty pages to disk and require transaction logs for reconstructing a consistent state. Known consistency-preserving methods suffer from excessive memory usage or a transaction-blocking behavior. In this paper, we present a consistency-preserving and memory-efficient checkpoint method. It is based on tuple shadowing as opposed to known page shadowing methods, and rearranging of tuples between pages for minimal memory usage overhead. The method’s algorithms are introduced and both analytical and experimental analysis of the proposed algorithms show significant reduction in the memory usage overhead, and up to 30% higher transaction throughput compared with a fuzzy checkpoint method with undo/redo log.

In data-intensive industrial on-line applications utilizing live process data, one faces an unusual set of database requirements. The process measurement data need to be acquired at great speed, organized in time series and made available for time-based retrieval. Active capabilities and functional extensibility are needed to implement a flexible data-driven processing paradigm. An efficient transaction logging and recovery mechanism is needed in order not to impede the data acquisition flow. RapidBase is a system that meets these requirements. It utilizes a main-memory database, a unique temporal-relational data model for handling time series, and an elaborate trigger subsystem. It is implemented as a server program equipped with interfaces of high power of expression.

Traditional event-condition-action triggers (active database rules) include a Boolean predicate as a trigger condition. We propose fuzzy triggers whereby fuzzy inference is utilized in the condition evaluation. In this way, approximate reasoning may be integrated with a traditional crisp database. The new approach paves the way for intuitive expression of application semantics of imprecise nature, in database-bound applications. Two fuzzy trigger models are proposed. Firstly, a set of fuzzy rules is encapsulated into a Boolean-valued function called a rule set function, leading to the C-fuzzy trigger model. Subsequently, actions are expressed also in fuzzy terms, and the corresponding CA-fuzzy trigger model is proposed. Examples are provided to illustrate how fuzzy triggers can be applied to a real-life drive control system in an industrial installation.

Algorithms for scheduling of distributed transactions in a heterogeneous multidatabase, in the presence of failures, are presented. The algorithms of prepare certification and commit certification protect against serialization errors called global view distortions and local view distortions. View serializable overall histories are guaranteed in the presence of most typical failures. The assumptions are, among others, that the participating database systems produce rigorous histories and that no local transaction may update the data accessed by a global transaction that is in the prepared state. The main advantage of the method, as compared to other known solutions, is that it is totally decentralized.>

LINDA, an experimental system for database access in heterogeneous environments, is discussed. The goal is to achieve maximum site autonomy and as much database access homogenization as possible. The problems to be solved are discussed, and design priorities are justified. Major heterogeneity problems are sketched, and the applied techniques are briefly described. An overview of the implementation of the system is given, and the system's applicability is discussed.>