Rewriting Algorithm Research Articles

A Differential Datalog Interpreter

The core reasoning task for datalog engines is materialization, the evaluation of a datalog program over a database alongside its physical incorporation into the database itself. The de-facto method of computing is through the recursive application of inference rules. Due to it being a costly operation, it is a must for datalog engines to provide incremental materialization; that is, to adjust the computation to new data instead of restarting from scratch. One of the major caveats is that deleting data is notoriously more involved than adding since one has to take into account all possible data that has been entailed from what is being deleted. Differential dataflow is a computational model that provides efficient incremental maintenance, notoriously with equal performance between additions and deletions, and work distribution of iterative dataflows. In this paper, we investigate the performance of materialization with three reference datalog implementations, out of which one is built on top of a lightweight relational engine, and the two others are differential-dataflow and non-differential versions of the same rewrite algorithm with the same optimizations. Experimental results suggest that monotonic aggregation is more powerful than ascenting merely the powerset lattice.

InDe: An Inline Data Deduplication Approach via Adaptive Detection of Valid Container Utilization

Inline deduplication removes redundant data in real-time as data is being sent to the storage system. However, it causes data fragmentation: logically consecutive chunks are physically scattered across various containers after data deduplication. Many rewrite algorithms aim to alleviate the performance degradation due to fragmentation by rewriting fragmented duplicate chunks as unique chunks into new containers. Unfortunately, these algorithms determine whether a chunk is fragmented based on a simple pre-set fixed value, ignoring the variance of data characteristics between data segments. Accordingly, when backups are restored, they often fail to select an appropriate set of old containers for rewrite, generating a substantial number of invalid chunks in retrieved containers. To address this issue, we propose an inline deduplication approach for storage systems, called InDe , which uses a greedy algorithm to detect valid container utilization and dynamically adjusts the number of old container references in each segment. InDe fully leverages the distribution of duplicated chunks to improve the restore performance while maintaining high backup performance. We define an effectiveness metric, valid container referenced counts (VCRC) , to identify appropriate containers for the rewrite. We design a rewrite algorithm F-greedy that detects valid container utilization to rewrite low-VCRC containers. According to the VCRC distribution of containers, F-greedy dynamically adjusts the number of old container references to only share duplicate chunks with high-utilization containers for each segment, thereby improving the restore speed. To take full advantage of the above features, we further propose another rewrite algorithm called F-greedy+ based on adaptive interval detection of valid container utilization. F-greedy+ makes a more accurate estimation of the valid utilization of old containers by detecting trends of VCRC’s change in two directions and selecting referenced containers in the global scope. We quantitatively evaluate InDe using three real-world backup workloads. The experimental results show that compared with two state-of-the-art algorithms (Capping and SMR), our scheme improves the restore speed by 1.3×–2.4× while achieving almost the same backup performance.

Privacy-aware electronic society

Our electronic society is making fast progress for offering users greater comfort in their daily activities. Users can benefit from fast access to services; better reactivity of their physical, computing, and networking environments to their own needs and habits; and preselection of information flows and relationships in which they are interested. Our digital society is eager to collect and analyze information for serving users as best as possible. Profiling users is a usual marketing activity, but new lucrative applications exploiting the huge amount of available information are emerging. The trend of Big Data has been accompanied by concerns regarding the aggregation, dissemination, and re-identification of personal identifiable information. This special issue contributes to address these privacy issues, proposing privacy-enhancing technologies and solutions suited to the today’s electronic society. The majority of the selected papers in this issue propose techniques and/or models for supporting privacy preferences and privacy-aware access control. The special issue also includes a contribution illustrating how the web browsing history can uniquely identify users for profiling purpose. Finally, there is a usercentered study discussing how much privacy people are willing to lose in order to gain some utilities. Collectively, these papers consider diverse contexts, ranging from social networks and web browsing to medical/social environments and Internet of Things (IoT). Said Oulmakhzoune, Nora Cuppens-Boulahia, Frederic Cuppens, Stephane Morucci, Mahmoud Barhamgi, and Djamal Benslimane propose a lightweight vocabulary for the fine-grained definition of privacy preferences in the paper titled “Privacy Query Rewriting Algorithm Instrumented by a Privacy-Aware Access Control Model.” Data owners express their privacy requirements through the PrivOrBAC privacy-aware model, in terms of restrictions on consent, accuracy, purpose, and recipients. SPARQL queries submitted by users are then rewritten in such a way that the query results comply with data owners’ privacy preferences. This rewriting process exploits PrivOrBAC privacy-aware model by decomposing SPARQL queries into a set of PrivOrBAC web services representing privacy preferences that must be satisfied. An interesting use case is also illustrated, showing the applicability of the approach in a healthcare scenario. The paper “PriMa: A Comprehensive Approach to Privacy Protection in Social Network Sites” by Anna Squicciarini, Federica Paci, and Smitha Sundareswaran, proposes a privacy protection mechanism for social network (SN) users named “Privacy Manager” (PriMa). PriMa helps users, through dynamically generated access rules, to preserve the privacy of their traits, that is, their pieces of information exposed to other SN users. The rules are generated based on three criteria as follows: (1) the level of accessibility of the user’s traits, (2) the profile of the owner’s privacy preference for the specific trait or for the trait’s category, and (3) the risks of unwanted or uncontrolled leakage of traits resulting from connected users who are eligible to access the profile owner’s traits. PriMa is implemented and the performance evaluation demonstrates that access rule generation takes reasonable time less than 0.7 ms for 150 traits and 260 users. The paper “Improving the User Content Privacy on Social Networks Using Rights Management Systems,” by Joaquim Marques and Carlos Serrao, aims at protecting users’ privacy in SNs. To overcome privacy issues arising when users share M. Laurent (*) Telecom SudParis, Evry, France e-mail: maryline.laurent@telecom-sudparis.eu

Congruence classes with logic variables

We are improving equality reasoning in automatic theorem-provers, and congruence classes provide an ecient storage mechanism for terms, as well as the congruence closure decision procedure. We describe the technical steps involved in integrating logic variables with congruence classes, and present an algorithm that can be proved to nd all matches between classes (modulo certain equalities). An application of this algorithm makes possible a percolation algorithm for undirected rewriting in minimal space; this is described and an implementation in hol98 is examined in some detail. 1