The Log-Structured Merge-Tree (LSM-tree) has been widely adopted for use in modern NoSQL systems for its superior write performance. Despite the popularity of LSM-trees, they have been criticized for suffering from write stalls and large performance variances due to the inherent mismatch between their fast in-memory writes and slow background I O operations. In this paper, we use a simple yet effective two-phase experimental approach to evaluate write stalls for various LSM-tree designs. We further explore the design choices of LSM merge schedulers to minimize write stalls given a disk bandwidth budget. We have conducted extensive experiments in the context of the Apache AsterixDB system and we present the results here.
Performance stability has long been recognized as a critical performance metric. The TPC-C benchmark @cite_9 measures not only absolute throughput, but also specifies the acceptable upper bounds for the percentile latencies of the transactions. @cite_40 applied VProfiler @cite_0 to identify major sources of variance in database transactions and proposed a variance-aware transaction scheduling algorithm. @cite_10 proposed techniques to optimize parameterized queries while balancing the average and variance of query cost. To reduce the variance of query processing, most existing proposals have either emphasized the use of table scans @cite_14 @cite_31 @cite_42 or stuck to worst-case query plans @cite_2 @cite_44 . Cao @cite_8 conducted an experimental study of the performance variance of modern storage stacks; they found that variance is common in storage stacks and heavily depends on configurations and workloads. Dean and Barroso @cite_30 discussed several engineering techniques to reduce performance variance in large-scale distributed systems at Google. Different from these efforts, in this work we focus on evaluating and minimizing the performance variances of LSM-trees due to their inherent out-of-place update design.
@cite_30 @cite_14 @cite_8 @cite_9 @cite_42 @cite_0 @cite_44 @cite_40 @cite_2 @cite_31 @cite_10
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While much of the research on transaction processing has focused on improving overall performance in terms of throughput and mean latency, surprisingly less attention has been given to performance predictability: how often individual transactions exhibit execution latency far from the mean. Performance predictability is increasingly important when transactions lie on the critical path of latency-sensitive applications, enterprise software, or interactive web services. In this paper, we focus on understanding and mitigating the sources of performance unpredictability in today's transactional databases. We conduct the first quantitative study of major sources of variance in MySQL, Postgres (two of the largest and most popular open-source products on the market), and VoltDB (a non-conventional database). We carry out our study with a tool called TProfiler that, given the source code of a database system and programmer annotations indicating the start and end of a transaction, is able to identify the dominant sources of variance in transaction latency. Based on our findings, we investigate alternative algorithms, implementations, and tuning strategies to reduce latency variance without compromising mean latency or throughput. Most notably, we propose a new lock scheduling algorithm, called Variance-Aware Transaction Scheduling (VATS), and a lazy buffer pool replacement policy. In particular, our modified MySQL exhibits significantly lower variance and 99th percentile latencies by up to 5.6 and 6.3, respectively. Our proposal has been welcomed by the open-source community, and our VATS algorithm has already been adopted as of MySQL's 5.7.17 release (and been made the default scheduling po Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Purpose  The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach  ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets  a citation network based on an early paper on webometrics, and a selfcitation network based on the 19 most cited papers in the Journal of Documentation  using citation data taken from the Web of Knowledge database.Findings  ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Scholars are increasingly citing electronic web references which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To webcite a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its instructions for authors accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) prospectively before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted citing articles (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. Most software profiling tools quantify average performance and rely on a program's control flow graph to organize and report results. However, in interactive server applications, performance predictability is often an equally important measure. Moreover, the end user is often concerned with the performance of a semantically defined interval of execution, such as a request or transaction, which may not directly map to any single function in the call graph, especially in high-performance applications that use asynchrony or event-based programming. It is difficult to distinguish functionality that lies on the critical path of a semantic interval from other activity (e.g., periodic logging or side operations) that may nevertheless appear prominent in a conventional profile. Existing profilers lack the ability to (i) aggregate results for a semantic interval and (ii) attribute its performance variance to individual functions. We propose a profiler called VProfiler that, given the source code of a software system and programmer annotations indicating the start and end of semantic intervals of interest, is able to identify the dominant sources of latency variance in a semantic context. Using a novel abstraction, called a variance tree, VProfiler analyzes the thread interleaving and deconstructs overall latency variance into variances and covariances of the execution time of individual functions. It then aggregates latency variance along a backwards path of dependence relationships among threads from the end of an interval to its start. We evaluate VProfiler's effectiveness on three popular open-source projects (MySQL, Postgres, and Apache Web Server). By identifying a few culprit functions in Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model selection criterion, often based on an estimator of generalisation performance, such as k-fold cross-validation. The error of such an estimator can be broken down into bias and variance components. While unbiasedness is often cited as a beneficial quality of a model selection criterion, we demonstrate that a low variance is at least as important, as a non-negligible variance introduces the potential for over-fitting in model selection as well as in training the model. While this observation is in hindsight perhaps rather obvious, the degradation in performance due to over-fitting the model selection criterion can be surprisingly large, an observation that appears to have received little attention in the machine learning literature to date. In this paper, we show that the effects of this form of over-fitting are often of comparable magnitude to differences in performance between learning algorithms, and thus cannot be ignored in empirical evaluation. Furthermore, we show that some common performance evaluation practices are susceptible to a form of selection bias as a result of this form of over-fitting and hence are unreliable. We discuss methods to avoid over-fitting in model selection and subsequent selection bias in performance evaluation, which we hope will be incorporated into best practice. While this study concentrates on cross-validation based model selection, the findings are quite general and apply to any model selection practice involving the optimisation of a model selection criterion evaluated over a finite sample of data, including maximisation o A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers.  2012 Wiley Periodicals, Inc.