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Journal articles on the topic 'Search process'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Johnson, Jean M., Ann Hillestad, Arthur D. Johnson, Rosemary McKevitt, Delight Tillotson, and Margaret Vancini. "Personalizing the search process." Journal of Professional Nursing 4, no. 4 (July 1988): 302–7. http://dx.doi.org/10.1016/s8755-7223(88)80019-x.

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2

Sharpe, S. "Search Warrants: Process Protection or Process Validation?" International Journal of Evidence & Proof 3, no. 2 (March 1999): 101–27. http://dx.doi.org/10.1177/136571279900300203.

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3

Greger, K. "Maximizing your executive-search process." Cornell Hotel and Restaurant Administration Quarterly 34, no. 6 (December 1993): 32–37. http://dx.doi.org/10.1016/0010-8804(93)90137-8.

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4

Bénichou, O., M. Moreau, P. H. Suet, and R. Voituriez. "Intermittent search process and teleportation." Journal of Chemical Physics 126, no. 23 (June 21, 2007): 234109. http://dx.doi.org/10.1063/1.2741516.

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5

Santovec, Mary Lou. "Search Process Emphasizes Preparation, Fit." Women in Higher Education 22, no. 2 (February 2013): 8–9. http://dx.doi.org/10.1002/whe.10423.

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Greger, Kenneth R. "Maximizing Your Executive-Search Process." Cornell Hotel and Restaurant Administration Quarterly 34, no. 6 (December 1993): 32–37. http://dx.doi.org/10.1177/001088049303400608.

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7

Yan, Zhiqiang, Remco Dijkman, and Paul Grefen. "Fast business process similarity search." Distributed and Parallel Databases 30, no. 2 (February 24, 2012): 105–44. http://dx.doi.org/10.1007/s10619-012-7089-z.

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8

Nijhof, Evert. "Subject analysis and search strategies – Has the searcher become the bottleneck in the search process?" World Patent Information 29, no. 1 (March 2007): 20–25. http://dx.doi.org/10.1016/j.wpi.2006.07.013.

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9

Yang, Chyan, Keng‐Chieh Yang, and Hsu‐Chieh Yuan. "Improving the search process through ontology‐based adaptive semantic search." Electronic Library 25, no. 2 (April 17, 2007): 234–48. http://dx.doi.org/10.1108/02640470710741359.

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10

Xie, Iris, and Soohyung Joo. "Transitions in search tactics during the Web-based search process." Journal of the American Society for Information Science and Technology 61, no. 11 (July 23, 2010): 2188–205. http://dx.doi.org/10.1002/asi.21391.

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11

Han, Suk Won. "Search for Capacity-Limited and Super-Capacity Search." Experimental Psychology 64, no. 3 (May 2017): 149–58. http://dx.doi.org/10.1027/1618-3169/a000356.

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Abstract. The present study investigated capacity limitations of visual search. In a series of experiments, participants searched for a singleton target among homogenous distractors, a conjunction target defined by combination of two features, or a feature target among heterogeneous distractors. Using the simultaneous-sequential paradigm, I found that singleton search proceeded in a capacity-unlimited manner. By contrast, the performance of the conjunction search was found to depend on a capacity-limited process. For feature searches, the performance of searching for a specific color was not affected by how the stimuli were presented, while the orientation search performance was enhanced as the number of distractors simultaneously presented with the target increased. These results imply that distinct colors are individually coded, whereas multiple orientations are encoded as an ensemble in a structured way. Taken together, the present study clarifies which type of search process are capacity-limited and reveals how this limit can be overcome.

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Savickas, Titas. "IMPROVEMENT OF SEARCH PROCESS IN ELECTRONIC CATALOGUES / PAIEŠKOS ELEKTRONINIUOSE KATALOGUOSE VEIKLOS PROCESO TOBULINIMAS." Mokslas – Lietuvos ateitis 6, no. 2 (April 24, 2014): 151–56. http://dx.doi.org/10.3846/mla.2014.20.

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The paper presents investigation on search in electronic catalogues. The chosen problem domain is the search system in the electronic catalogue of Lithuanian Academic Libraries. The catalogue uses ALEPH system with MARC21 bibliographic format. The article presents analysis of problems pertaining to the current search engine and user expectations related to the search system of the electronic catalogue of academic libraries. Subsequent to analysis, the research paper presents the architecture for a semantic search system in the electronic catalogue that uses search process designed to improve search results for users. Straipsnyje analizuojama paieška elektroniniuose kataloguose. Pasirinkta dalykinė sritis – Lietuvos universitetų akademinių bibliotekų elektroninis katalogas. Straipsnyje pateikiamos esamos paieškos sistemos ALEPH problemos ir vartotojų poreikių tyrimo rezultatai. Išanalizuojamas MARC21 formatas ir galimi alternatyvūs paieškos būdai, naudojami bibliotekoje. Atlikus analizę siūloma sistemos architektūra ir paieškos procesas, kuriais bandoma padidinti paieškos elektroniniame kataloge efektyvumą ir užtikrinti vartotojų poreikių patenkinimą.

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13

Aliasghar, Omid, and Elizabeth L. Rose. "Where to search for process innovations?" Academy of Management Proceedings 2017, no. 1 (August 2017): 11253. http://dx.doi.org/10.5465/ambpp.2017.11253abstract.

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14

Kyoungsik Na and WonTae Choi. "Mental States in Information Search Process." Journal of Korean Library and Information Science Society 48, no. 3 (September 2017): 281–302. http://dx.doi.org/10.16981/kliss.48.3.201709.281.

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15

Скутнік, Ярослав. "In search of matrices — ongoing process." Artistic Culture. Topical Issues, no. 15(1) (May 29, 2019): 34–41. http://dx.doi.org/10.31500/1992-5514.15(1).2019.168996.

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16

Maggitti, Patrick G., Ken G. Smith, and Riitta Katila. "The complex search process of invention." Research Policy 42, no. 1 (February 2013): 90–100. http://dx.doi.org/10.1016/j.respol.2012.04.020.

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17

McNally, Mary Jane, and Carol C. Kuhlthau. "Information Search Process in Science Education." Reference Librarian 20, no. 44 (July 12, 1994): 53–60. http://dx.doi.org/10.1300/j120v20n44_06.

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18

Aliev, T. M., D. A. Demir, E. Iltan, and N. K. Pak. "Vector leptoquark search in the process." Journal of Physics G: Nuclear and Particle Physics 22, no. 5 (May 1, 1996): 611–19. http://dx.doi.org/10.1088/0954-3899/22/5/008.

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19

Zeira, Joseph. "Investment as a Process of Search." Journal of Political Economy 95, no. 1 (February 1987): 204–10. http://dx.doi.org/10.1086/261449.

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20

McLaughlin, Judith Block, and David Riesman. "The Vicissitudes of the Search Process." Review of Higher Education 8, no. 4 (1985): 341–55. http://dx.doi.org/10.1353/rhe.1985.0013.

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21

Stratton, R. E. "A study in the search process." Rhetoric Review 5, no. 1 (September 1986): 99–105. http://dx.doi.org/10.1080/07350198609359140.

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22

Gilden, David L., Thomas L. Thornton, and Laura R. Marusich. "The serial process in visual search." Journal of Experimental Psychology: Human Perception and Performance 36, no. 3 (June 2010): 533–42. http://dx.doi.org/10.1037/a0016464.

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23

Narendranathan, Wiji, and Stephen Nickell. "Modelling the process of job search." Journal of Econometrics 28, no. 1 (April 1985): 29–49. http://dx.doi.org/10.1016/0304-4076(85)90065-x.

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24

Fan, Chengze. "Schooling as a job search process." Economics Letters 41, no. 1 (January 1993): 85–91. http://dx.doi.org/10.1016/0165-1765(93)90117-u.

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25

Shah, Chirag, Chathra Hendahewa, and Roberto González-Ibáñez. "Rain or shine? Forecasting search process performance in exploratory search tasks." Journal of the Association for Information Science and Technology 67, no. 7 (May 13, 2015): 1607–23. http://dx.doi.org/10.1002/asi.23484.

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26

Šormaz, Dušan N., and Behrokh Khoshnevis. "Process sequencing and process clustering in process planning using state space search." Journal of Intelligent Manufacturing 7, no. 3 (June 1996): 189–200. http://dx.doi.org/10.1007/bf00118079.

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27

Løkketangen, Arne. "Tabu Search – Using the Search Experience to Guide the Search Process. An Introduction with Examples." AI Communications 8, no. 2 (1995): 78–85. http://dx.doi.org/10.3233/aic-1995-8202.

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28

Terjesen, Siri, and Pankaj C. Patel. "In Search of Process Innovations: The Role of Search Depth, Search Breadth, and the Industry Environment." Journal of Management 43, no. 5 (March 18, 2015): 1421–46. http://dx.doi.org/10.1177/0149206315575710.

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Although a significant corpus of work focuses on the impact of search strategies on product innovations, we have a limited understanding of search strategies for process innovations, including the potential role of the industry environment. Process innovations are central to improving a firm’s productivity and contributing to efficiency and gross domestic product growth. As a result of the complexity of identifying, developing, and implementing process innovations, firms increasingly draw on external sources of knowledge. Building on key tenets in the knowledge search, innovation, and industry environment literatures, we investigate search strategies, process innovations, and industry dynamics in a sample of 505 firms spanning 23 manufacturing industries. We find that search breadth is negatively related to process innovation outcomes and that search depth is positively related to process innovation outcomes. Furthermore, high industry process heterogeneity mitigates the negative impact of search breadth on process innovation such that firms employing broad search strategies in highly process heterogeneous industries are more likely to introduce process innovations. In industries with greater productivity growth, the positive relationship between search depth and process innovation is stronger.

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29

Raman, Ramanujam, and Michael M. Marefat. "Integrated process planning using tool/process capabilities and heuristic search." Journal of Intelligent Manufacturing 15, no. 2 (April 2004): 141–74. http://dx.doi.org/10.1023/b:jims.0000018030.40309.e0.

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30

Liu, Chang, and Yiming Wei. "The impacts of time constraint on users' search strategy during search process." Proceedings of the Association for Information Science and Technology 53, no. 1 (2016): 1–9. http://dx.doi.org/10.1002/pra2.2016.14505301051.

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31

Bakhtadze, Nataliya N., Vladimir V. Kulba, Vladimir A. Lototsky, Evgeny M. Maximov, and Boris V. Pavlov. "Fuzzy associative search procedure for process identification." IFAC Proceedings Volumes 43, no. 4 (2010): 302–6. http://dx.doi.org/10.3182/20100701-2-pt-4011.00052.

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32

Tran, Thanh, Daniel M. Herzig, and Günter Ladwig. "SemSearchPro – Using semantics throughout the search process." Journal of Web Semantics 9, no. 4 (December 2011): 349–64. http://dx.doi.org/10.1016/j.websem.2011.08.004.

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33

Denzel, Alexander, and Johannes Kästner. "Gaussian Process Regression for Transition State Search." Journal of Chemical Theory and Computation 14, no. 11 (October 23, 2018): 5777–86. http://dx.doi.org/10.1021/acs.jctc.8b00708.

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34

Mellor, Joseph, Ioana Grigoras, Pablo Carbonell, and Jean-Loup Faulon. "Semisupervised Gaussian Process for Automated Enzyme Search." ACS Synthetic Biology 5, no. 6 (March 30, 2016): 518–28. http://dx.doi.org/10.1021/acssynbio.5b00294.

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35

O’Keeffe, Kevin, Paolo Santi, Brandon Wang, and Carlo Ratti. "Urban sensing as a random search process." Physica A: Statistical Mechanics and its Applications 562 (January 2021): 125307. http://dx.doi.org/10.1016/j.physa.2020.125307.

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36

Kacker, D., T. Camis, and J. P. Allebach. "Electrophotographic process embedded in direct binary search." IEEE Transactions on Image Processing 11, no. 3 (March 2002): 243–57. http://dx.doi.org/10.1109/83.988958.

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37

Savickas, Titas. "Improvement Of Search Process In Electronic Catalogues." Mokslas - Lietuvos ateitis 6, no. 2 (April 24, 2014): 151–56. http://dx.doi.org/10.3846/mla.2014.020.

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38

OKUMOTO, Yasuhisa, and Yukari YAMAMOTO. "Optimization of Construction Process Using Tabu Search." Journal of the Society of Naval Architects of Japan 2003, no. 194 (2003): 219–25. http://dx.doi.org/10.2534/jjasnaoe1968.2003.194_219.

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39

LIPTAK, JOHN J. "Irrational Expectations in the Job Search Process." Journal of Employment Counseling 26, no. 1 (March 1989): 35–40. http://dx.doi.org/10.1002/j.2161-1920.1989.tb00930.x.

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40

Hong, Traci. "Internet health search: When process complements goals." Journal of the American Society for Information Science and Technology 63, no. 11 (October 16, 2012): 2283–93. http://dx.doi.org/10.1002/asi.22728.

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41

Lin, B., and D. C. Miller. "Tabu search algorithm for chemical process optimization." Computers & Chemical Engineering 28, no. 11 (October 2004): 2287–306. http://dx.doi.org/10.1016/j.compchemeng.2004.04.007.

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42

McKenna, Michael C. "Cloze procedure as a memory-search process." Journal of Educational Psychology 78, no. 6 (1986): 433–40. http://dx.doi.org/10.1037/0022-0663.78.6.433.

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43

Suzuki, Yoshinori, and Juan David Cortes. "A tabu search with gradual evolution process." Computers & Industrial Engineering 100 (October 2016): 52–57. http://dx.doi.org/10.1016/j.cie.2016.08.004.

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44

Chandradoss, Stanley D., Nicole T. Schirle, Malwina Szczepaniak, Ian J. MacRae, and Chirlmin Joo. "A Dynamic Search Process Underlies MicroRNA Targeting." Cell 162, no. 1 (July 2015): 96–107. http://dx.doi.org/10.1016/j.cell.2015.06.032.

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45

Bussert, Leslie. "Millennial Students’ Online Search Strategies are Associated With Their Mental Models of Search." Evidence Based Library and Information Practice 6, no. 3 (September 14, 2011): 77. http://dx.doi.org/10.18438/b8wp7c.

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Objective – To examine first-year college students’ information seeking behaviours and determine whether their mental models of the search process influence their ability to effectively search for and find scholarly materials. Design – Mixed methods including contextual inquiry, concept mapping, observation, and interviews. Setting – University of Baltimore, a public institution in Maryland, United States of America, offering undergraduate, graduate, and professional degrees. Subjects – A total of 21 first-year undergraduate students, ages 16 to 19 years, undertaking research assignments for which they chose to use online resources. Methods – First-year students were recruited in the fall of 2008 and met with the researcher in a university usability lab for about one hour over a three week period. The researcher observed and videotaped the students as they conducted research in their chosen search engines or article databases. The searches were captured using software, and students were encouraged to think aloud about their research process, search strategies, and anticipated search results. Observation sessions concluded with a 10-question interview incorporating a review of the keywords the student used, the student’s reflection on the success of his or her searches, and possible alternate keywords. The interview also offered prompts to help the researcher learn about students’ conceptualizations of search tools’ utilization of keywords to generate results. The researcher then asked the students to provide a visual diagram of the relationship between their search terms and the items retrieved in the search tool. Data were analyzed by identifying the 21 different search tools used by the students and categorizing all 210 searches and student diagrams for further analysis. A scheme similar to Guinee, Eagleton, and Hall’s (2003) characterized the student searches into four categories: simple single-term searches, topic plus focus searches, phrase searches, and advanced searches employing multiple Boolean operators. Students’ diagrams were put into three different groups: process view, hierarchical view, and network view. The researcher then analyzed the relationships between the students’ search behaviours and their mental models to develop further conclusions. Main Results – Analysis revealed that this population of students had a limited mental model of the search process and used narrow sets of fairly simple search strategies for retrieving information online. Search engines were used for the majority (61.9%) of total searches and 72.3% of those conducted in search engines were in Google. The majority of students (76%) began their search process with a search engine while other students began searching in online encyclopedias (10%) or online databases (14%). Academic Search Premiere was used for 73.8% of the database searches. Some students (5%) also performed searches in individual websites (6.3%), for an overall total of 224 searches conducted. Students performed four varieties of searches: simple searches using short phrases conveying a single concept (34% of total searches); topic plus focus searches using a single Boolean AND (30%); phrase searches consisting of multiple-word descriptive phrases or sentence fragments (17.4%); and advanced Boolean searches combining two or more distinct concepts (13.8%). Generally, students used the same search terms and structure whether they were in a search engine or database, particularly with phrase searches. Nearly 71% of the advanced Boolean searches were inappropriately formed, particularly when used in the databases. Of the few students employing Boolean logic beyond a single AND, only two used it correctly, and only one with successful results. Students were unable to recognize or explain why a search failed or why they got the results they did. They made frequent incorrect use of punctuation, spelling, and syntax, leading to limited or no search results. Students assumed that obtaining few results indicated a problem of keyword choices rather than search query structure. When faced with no results in the databases, they assumed there were no articles on their topics and did not re-evaluate their search queries. Those with unsuccessful Boolean searches did not recognize that their errors were due to logic, and instead changed their keywords or began a new search altogether. Several students understood keywords as concepts versus literal strings of letters, yet thought the search tools determined search results based solely on what was typed into the search box. Of those employing phrase searches, some believed that each word was queried, while others thought only the “primary terms” were queried. Most students (61%) offered analogies to print resources to explain how search engines process queries, and all the students’ descriptions included their ideas about what a search tool contained, rather than how the search tools organized information. Attempts to expand or narrow searches were haphazard. While most students (57%) employed the strategy of adding keywords to narrow searches, only a few (11%) recognized the function of this technique and used it regularly, while others tended to return to their original broad searches in a different tool. Some had a limited understanding that adding terms narrows and reducing terms broadens the search, but their Boolean errors negated the use of synonyms or alternative terms for those purposes. Other strategies included using the search tool’s “advanced search” features or quotes, although all who used the latter did so incorrectly and some mistakenly thought parentheses served the same purpose. All subjects drew representations of their views of the relationship between keywords used and search results retrieved, though few were able to clearly visualize how a search engine processes a query, or address ideas such as expanding or narrowing searches or synonym use. Three categories of diagrams emerged: the process view, hierarchical view, and network view. The process view displayed a task flow diagram. These students demonstrated the least formed mental models and experienced the search tool as a “black box” that gives results, showing little understanding of how they are generated. They performed the fewest overall searches (11.6%), the majority (79%) of which were simple or phrase searches with no use of Boolean operators. The hierarchical view displayed a broad subject with subtopics, or results highlighting specific aspects of the subject. These students performed nearly 30% of the total searches, 17% of which included the use of Boolean logic. The network view displayed models of interconnected terms. These students performed the majority of the searches (58.7%), and also constructed the most sophisticated queries. Many of their searches employed Boolean logic (83%), and 65% were either Boolean or topic plus focus searches. Students with this mental model tended to focus more on the queries themselves than the results received. Students indicated feelings of success in their searching and were comfortable relying on simple searches retrieving large results sets. While not central to the research design or driving questions, students’ evaluation of search results was observed and found to be weak. Students displayed rapid searching, scanning, and evaluation processes which may have played a role in many of their mistakes when repeating or attempting to correct faulty searches. Conclusion – The results show students did not have strong conceptual models of the search process or how search queries impacted results, and were often unable to recognize or troubleshoot problems with searches in order to improve results. Students displaying stronger mental models used more complex search strategies, but still performed unsuccessful searches and demonstrated challenges in remedying defective searches. Students skimmed search results quickly, rarely looking beyond the first two pages, and did not take time to evaluate them for topic relevance. The findings suggest that librarians should rethink how Millennial students are taught search strategies and evaluation, to focus more on problem solving or critical thinking. They also suggest that database developers should continue developing search algorithms and tools, considering this population’s conceptualizations of search. Further research on Millennials’ information processing, critical thinking, and evaluation skills in the context of academic work is needed.

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46

Joo, Woo-Min, and Jin Young Choi. "Tabu Search-Genetic Process Mining Algorithm for Discovering Stochastic Process Tree." Journal of Society of Korea Industrial and Systems Engineering 42, no. 4 (December 31, 2019): 183–93. http://dx.doi.org/10.11627/jkise.2019.42.4.183.

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47

Brown, K. N., and J. Cagan. "Optimized process planning by generative simulated annealing." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, no. 3 (June 1997): 219–35. http://dx.doi.org/10.1017/s0890060400003140.

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AbstractManufacturing process planning is a difficult problem with a prohibitively large search space. It is normally tackled by decomposing goal objects into features, and then sequencing features to obtain a plan. This paper investigates an alternative approach. The capabilities of a manufacturing process are represented by a formal language of shape, in which sentences correspond to manufacturable objects. The language is interpreted to describe process plans corresponding to the shape generation, complete with cost estimates. A macro layer that describes single operations of the machine is implemented on top of the formal language. The space it describes is searched by the generative simulated annealing algorithm, a stochastic search technique based on simulated annealing. Plans that are close to the optimum are generated in reasonable time.

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48

Durairaj, M., and S. Gowri. "Gravitational Search Algorithm - Based Optimization of Process Parameters in Micro Turning Process." Applied Mechanics and Materials 592-594 (July 2014): 391–94. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.391.

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Micro turning is a scaled down version of conventional turning process, but operating on the micro scale of machining parameters to produce micro components. This paper deals with CNC Micro turning of Inconel 600 alloy with titanium carbide coated tool. Two conflicting objectives, surface roughness and tool flank wear, are simultaneously optimized. Full factorial experiments were taken with several combinations of cutting speed, feed and depth of cut. In this report, a new optimization algorithm based on the law of gravitation and mass interactions, namely Gravitational Search Algorithm (GSA) is aimed to predict the optimal parameter conditions for controlling tool flank wear and better surface finish.

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49

HU, He-jun, and Ming-yu FAN. "Hidden process detection technique based on memory search." Journal of Computer Applications 29, no. 1 (June 25, 2009): 175–77. http://dx.doi.org/10.3724/sp.j.1087.2009.00175.

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50

Kuhlthau, Carol C. "The Information Search Process: From Theory to Practice." Journal of Education for Library and Information Science 31, no. 1 (1990): 72. http://dx.doi.org/10.2307/40323730.

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