Parallel Implementation of Relational Algebra Operations on a Multi-Comparand Associative Machine

In this paper, we propose a new multi-comparand associative machine (MCA-machine) and its application to relational algebra operations. We first offer a new efficient associative algorithm for the multi-comparand parallel search. It generalizes the Falkoff associative algorithm that performs a parallel search in a matrix based on the exact match with a given pattern. Then we apply the new associative algorithm to implement one group of the relational algebra operations on the MCA-machine. Then, we propose efficient associative algorithms for implementing another group of the relational algebra operations. The proposed algorithms are represented as corresponding procedures for the MCA-machine. We prove their correctness and evaluate their time complexity

Multi-Comparand Associative Machine and its Application to Relational Algebra Operations

In this paper, we propose a new multi-comparand associative machine (MCA-machine) and its application to relational algebra operations. We first offer a new efficient associative algorithm for the multi-comparand parallel search. It generalizes the Falkoff associative algorithm that performs a parallel search in a matrix based on the exact match with a given pattern. Then we apply the new associative algorithm to implement a group of the relational algebra operations on the MCA-machine. The proposed algorithms are represented as corresponding procedures for the MCA-machine. We prove their correctness and evaluate their time complexity

Producing approximate answers to database queries

We have designed and implemented a query processor, called APPROXIMATE, that makes approximate answers available if part of the database is unavailable or if there is not enough time to produce an exact answer. The accuracy of the approximate answers produced improves monotonically with the amount of data retrieved to produce the result. The exact answer is produced if all of the needed data are available and query processing is allowed to continue until completion. The monotone query processing algorithm of APPROXIMATE works within the standard relational algebra framework and can be implemented on a relational database system with little change to the relational architecture. We describe here the approximation semantics of APPROXIMATE that serves as the basis for meaningful approximations of both set-valued and single-valued queries. We show how APPROXIMATE is implemented to make effective use of semantic information, provided by an object-oriented view of the database, and describe the additional overhead required by APPROXIMATE

Authorization algorithms for permission-role assignments

Permission-role assignments (PRA) is one important process in Role-based access control (RBAC) which has been proven to be a flexible and useful access model for information sharing in distributed collaborative environments. However, problems may arise during the procedures of PRA. Conflicting permissions may assign to one role, and as a result, the role with the permissions can derive unexpected access capabilities. This paper aims to analyze the problems during the procedures of permission-role assignments in distributed collaborative environments and to develop authorization allocation algorithms to address the problems within permission-role assignments. The algorithms are extended to the case of PRA with the mobility of permission-role relationship. Finally, comparisons with other related work are discussed to demonstrate the effective work of the paper

A relational dataflow database

A model of a relational database system based on the principles of functional, data-driven computation is proposed. Relations (sets of data tuples) are represented as streams of values carried by independent tokens among operators of an unraveling dataflow network.Values may be “updated” by circulating the database through an update operator. To perform a query on the database, streams involved in that query are replicated and submitted as inputs to dataflow programs (graphs) obtained by translating relational algebra expressions.

Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms

Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.Comment: 30th IEEE International Symposium on Computer-Based Medical Systems - IEEE CBMS 201

Implementing Relational-Algebraic Operators for Improving Cognitive Abilities in Networks of Neural Cliques

International audienceAssociative memories are devices capable of retrieving previously stored messages from parts of their content. They are used in a variety of applications including CPU caches, routers, intrusion detection systems, etc. They are also considered a good model for human memory, motivating the use of neural-based techniques. When it comes to cognition, it is important to provide such devices with the ability to perform complex requests, such as union, intersection, difference, projection and selection. In this paper, we extend a recently introduced associative memory model to perform relational algebra operations. We introduce new algorithms and discuss their performance which provides an insight on how the brain performs some high-level information processing tasks

Evaluating aggregate functions on possibilistic data

The need for extending information management systems to handle the imprecision of information found in the real world has been recognized. Fuzzy set theory together with possibility theory represent a uniform framework for extending the relational database model with these features. However, none of the existing proposals for handling imprecision in the literature has dealt with queries involving a functional evaluation of a set of items, traditionally referred to as aggregation. Two kinds of aggregate operators, namely, scalar aggregates and aggregate functions, exist. Both are important for most real-world applications, and are thus being supported by traditional languages like SQL or QUEL. This paper presents a framework for handling these two types of aggregates in the context of imprecise information. We consider three cases, specifically, aggregates within vague queries on precise data, aggregates within precisely specified queries on possibilistic data, and aggregates within vague queries on imprecise data. These extensions are based on fuzzy set-theoretical concepts such as the extension principle, the sigma-count operation, and the possibilistic expected value. The consistency and completeness of the proposed operations is shown