Technical Perspective

Byte-addressable persistent memory was considered in the data management community as long ago as 1986. Thatte saw the advantages for programmability in unifying the abstractions of byte-addressable RAM with persistence [2]. Thatte's context was object-oriented databases containing a variety data structures that would be awkward to transform into the block-oriented abstractions provided by typical secondary storage. Thatte's proposed physical instantiation of persistent memory was a disk-backed device, although it is unclear whether such a device was ever built. Thatte recognized the importance of recovery to the overall scheme.

Ext-LOUDS: A Space Efficient Extended LOUDS Index for Superset Query

Superset query is widely used in object-oriented databases, data mining, and many other fields. Trie is an efficient index for superset query, whereas most existing trie index aim at improving query performance while ignoring storage overheads. To solve this problem, in this paper, we propose an efficient extended Level-Ordered Unary Degree Sequence (LOUDS) index: Ext-LOUDS. Ext-LOUDS expresses a trie by 1 integer vector and 3 bit vectors directly map each NodeID to its corresponding position, thus accelerating some key operations needed for superset query. Based on Ext-LOUDS, an efficient superset query algorithm, ELOUDS-Super, is designed. Experimental results on both real and synthetic datasets show that Ext-LOUDS can decrease 50%–60% space overheads compared with trie while maintaining a relative good query performance.

Using object-oriented databases in face recognition

The aim of the work is to develop an algorithm functioning by a face recognition system using object-oriented databases. The system provides automatic identification of the desired object or identifies someone using a digital photo or video frame from a video source. The technology includes comparing pre-scanned face elements from the resulting image with prototypes of faces stored in the database. Modern packages of object-oriented databases give the user the opportunity to create a new class with the specified attributes and methods, obtain classes that inherit attributes and methods from super classes, create instances of the class, each of which has a unique object identifier, extract these instances one by one or in groups, and also download and perform these procedures. Using a convolutional neural network in the algorithm allows the transition from specific features of the image to more abstract details.

Modeling Environmental Impacts on Viticultural Ecosystems

This paper focuses on simulating environmental impacts on grapevine behavioral dynamics and vineyard management strategies. The methodology presented uses technology from geomatics object oriented databases and spatio-temporal data models. Our approach has two principle objectives, first, to simulate grapevine phenology and grape ripening under spatial and temporal environmental conditions and constraints and secondly, to simulate viticultural practices and adaptation strategies under various constraints (environmental, economical, socio-technical). The approach is based on a responsive agent-based structure where environmental conditions and constraints are considered as a set of forcing data (biophysical, socio-economic and regulatory data) that influences the modelled activities. The experiment was conducted in the regulated wine producing appellation Grand Cru “Quarts de Chaume”, situated in the middle Loire Valley, France. All of the methodology, from the implementation of the knowledge database to the analysis of the first simulation, is presented in this paper.