Online B-tree merging

Sun, Xiaowei; Wang, Rui; Salzberg, Betty; Zou, Chendong

doi:10.1145/1066157.1066196

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…Two old papers Two recent papers One old and one recent paper [11] and [18] [25] and [39] [2] and [30] Similarity measures [12] and [30] [5] and [6] [24] and [32] [15] and [29] [5] and [40] [33] and [37] [31] and [33] [6] and [40] [7] and [29] Table 5, the highest-scoring papers by C-Rank are mostly related to clustering. The results show that CRank can provide a set of papers similar to the paper under consideration.…”

Section: Top 10 Rankingsmentioning

confidence: 99%

“…We also show that C-Rank properly computes the similarity score in all three cases. For demonstration purposes, we select ( [11,18]), ( [12,30]), ( [15,29]) and ( [31,33]) as pairs of old but similar papers, ( [25,39]), ( [5,6]), ( [5,40]), and ( [6,40]) as pairs of recent but similar papers, and ( [2,30]), ( [24,32]), ( [33,37]), and ( [7,29]) as pairs of an old paper and a recent one that are similar. Table 6 shows the result of this case analysis.…”

Section: Failure Of Existing Similarity Measuresmentioning

confidence: 99%

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C-Rank: A link-based similarity measure for scientific literature databases

Yoon

Kim

Park

2016

Information Sciences

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a b s t r a c tAs the number of people who use scientific literature databases has grown, the demand for literature retrieval services has steadily increased. One of the most popular retrieval service methods is to find a set of papers similar to the paper under consideration, which requires a measure that computes the similarities between the papers. Scientific literature databases exhibit two interesting characteristics that are not found in general databases. First, the papers cited by older papers are often not included in the database due to technical and economic reasons. Second, since a paper references previously published papers, few papers cite recently published papers. These two characteristics cause all existing similarity measures to fail in at least one of the following cases: (1) measuring the similarity between old, but similar papers, (2) measuring the similarity between recent, but similar papers, and (3) measuring the similarity between two similar papers: one old, the other recent. In this paper, we propose a new link-based similarity measure called C-Rank, which uses both in-link and out-link references, disregarding the direction of the references. In addition, we discuss the most suitable normalization method for scientific literature databases and we propose an evaluation method for measuring the accuracy of similarity measures. For the experiments, we used real-world papers from DBLP's database with reference information crawled from Libra. We then compared the performance of C-Rank with that of existing similarity measures. Experimental results showed that C-Rank achieved a higher accuracy than existing similarity measures.

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Section: Top 10 Rankingsmentioning

confidence: 99%

Section: Failure Of Existing Similarity Measuresmentioning

confidence: 99%

C-Rank: A link-based similarity measure for scientific literature databases

Yoon

Kim

Park

2016

Information Sciences

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“…Sun et al use laziness for online merging of B-trees [17], which is a special case of our approach. The idea is to let merge operations piggyback on user transactions to reduce I/O operations.…”

Section: Related Workmentioning

confidence: 99%

Lazy Evaluation for Concurrent OLTP and Bulk Transactions

Wevers

Huisman

Keulen

2016

Proceedings of the 20th International Database Engineering &Amp; Applications Symposium on - IDEAS '16

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Existing concurrency control systems cannot execute transactions with overlapping updates concurrently. This is especially problematic for bulk updates, which usually overlap with all concurrent transactions. To solve this, we have developed a concurrency control mechanism based on lazy evaluation, which moves evaluation of operations from the writer to the reader. This allows readers to prioritize evaluation of those operations in which they are interested, without loss of atomicity of transactions. To handle bulk operations, we dynamically split large transactions into transactions on smaller parts of the data. In this paper we present an abstract lazy index structure for lazy transactions, and show how transactions can be encoded to effectively use this data structure. Moreover, we discuss evaluation strategies for lazy transactions, where trade-offs can be made between latency and throughput. To evaluate our approach, we have implemented a concurrent lazy trie, on which we performed a number of micro benchmarks.

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“…Typically, the incremental reorganization executes as part of a user's transaction. An alternative is to reorganize in a separate system transaction and to have the user transaction wait for the system transaction [Sun et al 2005;Sun 2005, pp. 89-111].…”

Section: The Process That Reorganizesmentioning

confidence: 99%

“…Sun [2005, pp. 89-111] and Sun et al [2005] describe such merging. Because the indexes have the same key range, the merging cannot just attach them to each other.…”

Section: Merging Of Indexesmentioning

confidence: 99%

Online reorganization of databases

Sockut

Iyer

2009

ACM Comput. Surv.

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In practice, any database management system sometimes needs reorganization, that is, a change in some aspect of the logical and/or physical arrangement of a database. In traditional practice, many types of reorganization have required denying access to a database (taking the database offline) during reorganization. Taking a database offline can be unacceptable for a highly available (24-hour) database, for example, a database serving electronic commerce or armed forces, or for a very large database. A solution is to reorganize online (concurrently with usage of the database, incrementally during users' activities, or interpretively). This article is a tutorial and survey on requirements, issues, and strategies for online reorganization. It analyzes the issues and then presents the strategies, which use the issues. The issues, most of which involve design trade-offs, include use of partitions, the locus of control for the process that reorganizes (a background process or users' activities), reorganization by copying to newly allocated storage (as opposed to reorganizing in place), use of differential files, references to data that has moved, performance, and activation of reorganization. The article surveys online strategies in three categories of reorganization. The first category, maintenance, involves restoring the physical arrangement of data instances without changing the database definition. This category includes restoration of clustering, reorganization of an index, rebalancing of parallel or distributed data, garbage collection for persistent storage, and cleaning (reclamation of space) in a logstructured file system. The second category involves changing the physical database definition; topics include construction of indexes, conversion between B + -trees and linear hash files, and redefinition (e.g., splitting) of partitions. The third category involves changing the logical database definition. Some examples are changing a column's data type, changing the inheritance hierarchy of object classes, and changing a relationship from one-to-many to many-to-many. The survey encompasses both research and commercial implementations, and this article points out several open research topics. As highly available or very large databases continue to become more common and more important in the world economy, the importance of online reorganization is likely to continue growing.

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Online B-tree merging

Cited by 11 publications

References 22 publications

C-Rank: A link-based similarity measure for scientific literature databases

C-Rank: A link-based similarity measure for scientific literature databases

Lazy Evaluation for Concurrent OLTP and Bulk Transactions

Online reorganization of databases

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