Overlapping B+-trees: an Implementation of a Transaction Time Access Method

A new variation of Overlapping B+-trees is presented, which provides efficient indexing of transaction time and keys in a two dimensional key-time space. Modification operations (i.e. insertions, deletions and updates) are allowed at the current version, whereas queries are allowed to any temporal version, i.e. either in the current or in past versions. Using this structure, snapshot and range-timeslice queries can be answered optimally. However, the fundamental objective of the proposed method is to deliver efficient performance in case of a general pure-key query (i.e. "history of a key"). The trade-off is a small increase in time cost for version operations and storage requirements

Processing of Spatio-Temporal Queries in Image Databases

Overlapping Linear Quadtrees is a structure suitable for storing consecutive raster images according to transaction time (a database of evolving images). This structure saves considerable space without sacrificing time performance in accessing every single image. Moreover, it can be used for answering efficiently window queries for a number of consecutive images (spatio-temporal queries). In this paper, we present three such temporal window queries: strict containment, border intersect and cover. Besides, based on a method of producing synthetic pairs of evolving images (random images with specified aggregation) we present empirical results on the I/O performance of these queries

Bayesian Deep Learning with Trust and Distrust in Recommendation Systems

Exploiting the selections of social friends and foes can efficiently face the data scarcity of user preferences and the cold-start problem. In this paper, we present a Social Deep Pairwise Learning model, namely SDPL. According to the Bayesian Pairwise Ranking criterion, we design a loss function with multiple ranking criteria based on the selections of users, and those in their friends and foes to improve the accuracy in the top-k recommendation task. We capture the nonlinearity in user preferences and the social information of trust and distrust relationships by designing a deep learning architecture. In each backpropagation step, we perform social negative sampling to meet the multiple ranking criteria of our loss function. Our experiments on a benchmark dataset from Epinions, among the largest publicly available that has been reported in the relevant literature, demonstrate the effectiveness of the proposed approach, outperforming other state-of-the art methods. In addition, we show that our deep learning strategy plays an important role in capturing the nonlinear associations between user preferences and the social information of trust and distrust relationships, and demonstrate that our social negative sampling strategy is a key factor in SDPL

Efficient indexing of spatiotemporal objects

Abstract — Spatiotemporal objects i.e., objects which change their position and/or extent over time, appear in many applications. This paper addresses the problem of indexing large volumes of such data. We consider general object movements and extent changes. We further concentrate on “snapshot ” as well as small “interval ” historical queries on the gathered data. The obvious approach that approximates spatiotemporal objects with MBRs and uses a traditional multidimensional access method to index them is inefficient. Objects that “live ” for long time intervals have large MBRs which introduce a lot of empty space. Clustering long intervals has been dealt in temporal databases by the use of partially persistent indices. What differentiates this problem from traditional temporal indexing is that objects are allowed to move/change during their lifetime. Better methods are thus needed to approximate general spatiotemporal objects. One obvious solution is to introduce artificial splits: the lifetime of a long-lived object is split into smaller consecutive pieces. This decreases the empty space but increases the number of indexed MBRs. We first introduce two algorithms for splitting a given spatiotemporal object. Then, given an upper bound on the total number of possible splits, we present three algorithms that decide how the splits should be distributed among the objects so that the total empty space is minimized. I