Column Sketches Hentschel, Brian; Kester, Michael S.; Idreos, Stratos
Proceedings of the 2018 International Conference on Management of Data,
05/2018
Conference Proceeding
While numerous indexing and storage schemes have been developed to address the core functionality of predicate evaluation in data systems, they all require specific workload properties (query ...selectivity, data distribution, data clustering) to provide good performance and fail in other cases. We present a new class of indexing scheme, termed a Column Sketch, which improves the performance of predicate evaluation independently of workload properties. Column Sketches work primarily through the use of lossy compression schemes which are designed so that the index ingests data quickly, evaluates any query performantly, and has small memory footprint. A Column Sketch works by applying this lossy compression on a value-by-value basis, mapping base data to a representation of smaller fixed width codes. Queries are evaluated affirmatively or negatively for the vast majority of values using the compressed data, and only if needed check the base data for the remaining values. Column Sketches work over column, row, and hybrid storage layouts.
We demonstrate that by using a Column Sketch, the select operator in modern analytic systems attains better CPU efficiency and less data movement than state-of-the-art storage and indexing schemes. Compared to standard scans, Column Sketches provide an improvement of 3x-6x for numerical attributes and 2.7x for categorical attributes. Compared to state-of-the-art scan accelerators such as Column Imprints and BitWeaving, Column Sketches perform 1.4 - 4.8× better.
We live in an increasingly interconnected world, with many organizations operating across countries or even continents. To serve their global user base, organizations are replacing their legacy DBMSs ...with cloud-based systems capable of scaling OLTP workloads to millions of users. CockroachDB is a scalable SQL DBMS that was built from the ground up to support these global OLTP workloads while maintaining high availability and strong consistency. Just like its namesake, CockroachDB is resilient to disasters through replication and automatic recovery mechanisms. This paper presents the design of CockroachDB and its novel transaction model that supports consistent geo-distributed transactions on commodity hardware. We describe how CockroachDB replicates and distributes data to achieve fault tolerance and high performance, as well as how its distributed SQL layer automatically scales with the size of the database cluster while providing the standard SQL interface that users expect. Finally, we present a comprehensive performance evaluation and share a couple of case studies of CockroachDB users. We conclude by describing lessons learned while building CockroachDB over the last five years.
Distance measures are core building blocks in time-series analysis and the subject of active research for decades. Unfortunately, the most detailed experimental study in this area is outdated (over a ...decade old) and, naturally, does not reflect recent progress. Importantly, this study (i) omitted multiple distance measures, including a classic measure in the time-series literature; (ii) considered only a single time-series normalization method; and (iii) reported only raw classification error rates without statistically validating the findings, resulting in or fueling four misconceptions in the time-series literature. Motivated by the aforementioned drawbacks and our curiosity to shed some light on these misconceptions, we comprehensively evaluate 71 time-series distance measures. Specifically, our study includes (i) 8 normalization methods; (ii) 52 lock-step measures; (iii) 4 sliding measures; (iv) 7 elastic measures; (v) 4 kernel functions; and (vi) 4 embedding measures. We extensively evaluate these measures across 128 time-series datasets using rigorous statistical analysis. Our findings debunk four long-standing misconceptions that significantly alter the landscape of what is known about existing distance measures. With the new foundations in place, we discuss open challenges and promising directions.
Detecting Ponzi Schemes on Ethereum Chen, Weili; Zheng, Zibin; Cui, Jiahui ...
Proceedings of the 2018 World Wide Web Conference,
04/2018
Conference Proceeding
Odprti dostop
Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very ...negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.
Nowadays large-scale data-centric systems have become an essential element for companies to store, manipulate and derive value from large volumes of data. Capturing this value depends on the ability ...of these systems in managing large-scale workloads including complex analytical queries. One of the main characteristics of these queries is that they share computations in terms of selections and joins. Materialized views (MV) have shown their force in speeding up queries by exploiting these redundant computations. MV selection problem (VSP) is one of the most studied problems in the database field. A large majority of the existing solutions follow workload-driven approaches since they facilitate the identification of shared computations. Interesting algorithms have been proposed and implemented in commercial DBMSs. But they fail in managing large-scale workloads. In this paper, we presented a comprehensive framework to select the most beneficial materialized views based on the detection of the common subexpressions shared between queries. This framework gives the right place of the problem of selection of common subexpressions representing the causes of the redundancy. The utility of final MV depends strongly on the selected subexpressions. Once selected, a heuristic is given to select the most beneficial materialized views by considering different query ordering. Finally, experiments have been conducted to evaluate the effectiveness and efficiency of our proposal by considering large workloads.
The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds ...and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history, typically through recurrent or graph neural networks. However, fewer studies have explored how to incorporate principles of human learning at both the course and category levels to enhance course recommendations. In this article, we aim at addressing this gap by introducing a novel model, named Prerequisite-Enhanced Catory-Aware Graph Neural Network (PCGNN), for course recommendation. Specifically, we first construct a course prerequisite graph that reflects the human learning principles and further pre-train the course prerequisite relationships as the base embeddings for courses and categories. Then, to capture the user’s complex learning patterns, we build an item graph and a category graph from the user’s historical learning records, respectively: (1) the item graph reflects the course-level local learning transition patterns and (2) the category graph provides insight into the user’s long-term learning interest. Correspondingly, we propose a user interest encoder that employs a gated graph neural network to learn the course-level user interest embedding and design a category transition pattern encoder that utilizes GRU to yield the category-level user interest embedding. Finally, the two fine-grained user interest embeddings are fused to achieve precise course prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of PCGNN compared with other state-of-the-art methods.
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