Astronomy is well recognized as big data driven science. As the novel observation infrastructures are developed, the sky survey cycles have been shortened from a few days to a few seconds, causing data processing pressure to shift from offline to online. However, existing scientific databases focus on offline analysis of long-term historical data, not real-time and low latency analysis of large-scale newly arriving data.In this paper, a cloud based method is proposed to efficiently analyze scientific events on large-scale newly arriving data. The solution is implemented as a highly efficient system, namely Aserv. A set of compact data store and index structures are proposed to describe the proposed scientific events and a typical analysis pattern is formulized as a set of query operations. Domain aware filter, accuracy aware data partition, highly efficient index and frequently used statistical data designs are four key methods to optimize the performance of Aserv. Experimental results under the typical cloud environment show that the presented optimization mechanism can meet the low latency demand for both large data insertion and scientific event analysis. Aserv can insert 3.5 million rows of data within 3 seconds and perform the heaviest query on 6.7 billion rows of data also within 3 seconds. Furthermore, a performance model is given to help Aserv choose the right cloud resource setup to meet the guaranteed real-time performance requirement.
. Real-time databases have been studied since 1980s, and the key goal is to enable as many real-time transactions as possible to meet their respective time constraints @cite_19 . Real-time databases are more concern with timeliness, not system speed @cite_33 , due to a basis hypothesis that catastrophic consequences do not happen in the real world if a transaction is finished within the deadline. Hence, many of works focus on scheduling @cite_19 @cite_4 and transaction processing @cite_10 @cite_28 . Storing and processing all the data under periodic time constraint can avoid data loss and ensure temporal data consistency maybe enough for traditional real-time databases, but the transient feature of scientific event requires that the online query latency should be as low as possible. Only in this way, we can exploit the value of scientific data. Periodic survey cycle and the unpredictability of scientific event propose the new challenge for online big scientific data analysis.
@cite_4 @cite_33 @cite_28 @cite_19 @cite_10
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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. Scheduling transactions with real-time requirements presents many new problems. In this paper we discuss solutions for two of these problems: what is a reasonable method for modeling real-time constraints for database transactions? Traditional hard real-time constraints (e.g., deadlines) may be too limited. May transactions have soft deadlines and a more flexible model is needed to capture these soft time constraints. The second problem we address is scheduling. Time constraints add a new dimension to concurrency control. Not only must a schedule be serializable but it also should meet the time constraints of all the transactions in the schedule. 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 self‐citation 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 ... A real-time database system (RTDBS) is a database system designed to handle workloads whose state is constantly changing. This system differs from traditional databases containing persistent data, mostly unaffected by time. A real-time database is a database in which transactions have deadlines or timing constraints. Real-time databases are commonly used in real-time computing applications that require timely access to data. In this article, we will discuss the most important concepts of real-time database systems. Keywords: real-time database system; transaction processing; concurrency control