Searching in a Sorted Linked List and Sort Integers into a Linked List

Title from PDF of title page viewed June 12, 2019Thesis advisor: Yijie HanVitaIncludes bibliographical references (pages 25-27)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019The research work consists of two parts. Part one is about Searching for an integer in a sorted Linked list. A tree is constructed in O(nloglogm/p+loglogm) time with p processors based on the trie with all the given integers. Additional nodes (O(nloglogm) of them) are added to the tree. After the tree is constructed, for any given integer we can find the predecessor and successor of the integer, insert or delete the integer in O(loglogm) time. The result demonstrates for the searching purpose we need not to sort the input numbers into a sorted array for this would need at least O(logn/loglogn) time while this algorithm for constructing the tree can run in O(loglogm) time with n processors. Part two is on sorting integers into a linked list. There are various best algorithms for sorting integers. The current research work applies the recent important results of sorting integers in Ω(logn/loglogn) time. This algorithm takes “constant time” to sort integers into a linked list with nlogm processors and O(loglogm/logt) time using nt processors on the Priority CRCW PRAM model.Introduction -- Searching in a sorted linked list -- Sort integers into a linked list -- Conclusio

Constant Time Sorting and Searching

Title from PDF of title page, viewed January 4, 2023Thesis advisor: Yijie HanVitaIncludes bibliographical references (pages 25-28)Thesis (M.S.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2022To study the sorting of real numbers into a linked list on Parallel Random Access Machine model. To show that input array of n real numbers can be sorted into a linked list in constant time using n²/logᶜn processors for any positive constant c. The searching problem studied is locating the interval of n sorted real numbers for inserting a query real number. Taking into account an input of n real numbers and organize them in the sorted order to facilitate searching. Initially, sorting the n input real numbers and then convert these real numbers into integers such that their relative order is preserved. Convert the query input real number to a query integer and then search the interval among these n integers for the insertion point of this query real number in constant time.Introduction -- Sorting in constant time -- Searching in constant time -- Theorem -- Conclusion

Substring filtering for low-cost linked data interfaces

Recently, Triple Pattern Fragments (TPFS) were introduced as a low-cost server-side interface when high numbers of clients need to evaluate SPARQL queries. Scalability is achieved by moving part of the query execution to the client, at the cost of elevated query times. Since the TPFS interface purposely does not support complex constructs such as SPARQL filters, queries that use them need to be executed mostly on the client, resulting in long execution times. We therefore investigated the impact of adding a literal substring matching feature to the TPFS interface, with the goal of improving query performance while maintaining low server cost. In this paper, we discuss the client/server setup and compare the performance of SPARQL queries on multiple implementations, including Elastic Search and case-insensitive FM-index. Our evaluations indicate that these improvements allow for faster query execution without significantly increasing the load on the server. Offering the substring feature on TPF servers allows users to obtain faster responses for filter-based SPARQL queries. Furthermore, substring matching can be used to support other filters such as complete regular expressions or range queries

HELIN Federated Search Task Force Final Report

Final report of the HELIN Federated Search Task Force, a group appointed by the HELIN Reference Committee at the request of the HELIN Directors to investigate and report on available federated search engines, which allow users simultaneously to search multiple databases. The task force was not asked to recommend a specific one for licensing by HELIN member libraries and did not do so