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Статті в журналах з теми "Mobius search"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

PRAVEEN. P, PRAVEEN P. "MSPEUX – Mobile Search Personalization by Enhanced User Experience." International Journal of Scientific Research 3, no. 5 (June 1, 2012): 237–39. http://dx.doi.org/10.15373/22778179/may2014/71.

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2

Schwartz, Stephan Andrew. "The Discovery of an American Brig: Fieldwork Involving Applied Remote Viewing Including a Comparison with Electronic Remote Sensing." Journal of Scientific Exploration 34, no. 1 (March 23, 2020): 62–92. http://dx.doi.org/10.31275/20201481.

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In the fall of 1987 Mobius began fieldwork, under a license from the Bahamian Government, to carry out an archaeological survey in an area of the Grand Bahama Banks encompassing some 579.15 square miles (1500 sq. km). This report compares the Remote Viewing, electronic remote sensing, and visual search process used to locate the wreck site of a previously undiscovered armed American merchantman believed to be the Brig Leander, which was found in a sub-section of the License Area known as Consensus Zone C; an area of 11.81 sq. miles (30.59 sq. km) of water. It concludes that Remote Viewing was the source of information which led to the site’s location, and that electronic remote sensing was not useful in this instance. Leander was under the Command of Captain William Johnson when she sank for unknown reasons near Beaks Cay on 6 April 1834, while returning from Manzanilla, Cuba to her homeport in Boston, Massachusetts. In addition to location information, a total of 193 conceptual descriptive concepts concerning the site were proffered by twelve Remote Viewers. Of this, 148 concepts, or 75% of the total, could be evaluated through direct field observations, or historical research. An evaluation of this material reveals 84% Correct, 12% Partially Correct, 4% Incorrect. There is little accuracy variation across the sequence of material from the Los Angeles interviews (84% Corr., 13% Part. Co rr. ,3% Incorr.), to the on-site data (81% Corr., 11% Part. Corr., 8% Incorr.). Approximately 300 notable wrecks went down, not just in the License Area but across the entire Banks, from 1500 to 1876 as determined by a thorough search of historical records and archival material in the U.S., the U.K., Spain and the Bahamas. To make a conservative assessment of this location occurring by chance, assume the wrecks are evenly distributed not throughout the Banks, but only within the License Area. That said, we should expect to see 6.12 boats in Consensus Zone C (11.81/579.15 x 300 =6.12). The brig site is 5000 square feet (464.5 sq. m), equaling 0.00018 of a square mile. Within Consensus Zone C 65,849 sites of this size could be placed, thus yielding a grid of 65,849 cells.. If the probability of selecting this particular cell in the grid by chance exceeds p≥ 0.05 then Remote Viewing can be considered a determinative factor. The probability of finding this one 5,000 square feet area is then 6.12/65,849 = p 0.00009, which strongly suggests that chance is not an explanation for the location of Leander.History: “The Discovery of an American Brig: Fieldwork Involving Applied Archaeological Remote Viewing,” Parapsychological Association Conference 1988. Also, The Discovery of an American Brig: Fieldwork Involving Applied Archaeological Remote Viewing, Including a Comparison with Electronic Remote Sensing,” Conference on Underwater Archaeology/Society of Historic Archaeology Annual Meetings. 1989.

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3

Haider, Syed Waqar, Guijun Zhuang, Hammad bin Azam Hashmi, and Shahid Ali. "Chronotypes’ Task-Technology Fit for Search and Purchase in Omnichannel Context." Mobile Information Systems 2019 (March 25, 2019): 1–9. http://dx.doi.org/10.1155/2019/8968264.

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Advances in technology have led to a spurt in tech-savvy consumers displaying increasingly complex behavior. In the past, consumers concluded their search and purchases at a single physical store. Nowadays, however, they possess a number of digital devices (desktops and/or mobiles) through which they can switch channels seamlessly to search for information and make a purchase. There are very few studies that investigate desktop and mobile channels separately; this is perhaps the first study that examines the effect of chronotypes (evening- and morning-type individuals) on a sample of Chinese university students using online and mobile channels in the omnichannel process. The results from a sample of 311 digital shoppers (desktops and/or mobiles) confirm that the mobile channel offers greater task-technology fit to evening-type respondents and desktop channels present better task-technology fit to morning-type respondents to engage in the omnichannel process. Furthermore, this study discussed contributions and insights for managers to develop an effective omnichannel strategy.

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4

Shimamura, Jun. "Mobile Visual Search." Journal of the Institute of Image Information and Television Engineers 68, no. 3 (2014): 233–34. http://dx.doi.org/10.3169/itej.68.233.

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5

Girod, Bernd, Vijay Chandrasekhar, David Chen, Ngai-Man Cheung, Radek Grzeszczuk, Yuriy Reznik, Gabriel Takacs, Sam Tsai, and Ramakrishna Vedantham. "Mobile Visual Search." IEEE Signal Processing Magazine 28, no. 4 (July 2011): 61–76. http://dx.doi.org/10.1109/msp.2011.940881.

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6

Xiong, Zhuangzhi, Tao Xu, Yugang Dai, and Liang Chen. "Mobile Search Development Research." Journal of Physics: Conference Series 1087 (September 2018): 032021. http://dx.doi.org/10.1088/1742-6596/1087/3/032021.

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7

Paulson, L. D. "Search technology goes mobile." Computer 38, no. 8 (2005): 19–22. http://dx.doi.org/10.1109/mc.2005.270.

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8

Roto, Virpi. "Search on mobile phones." Journal of the American Society for Information Science and Technology 57, no. 6 (2006): 834–37. http://dx.doi.org/10.1002/asi.20303.

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9

Park, So-Yeon. "Analysis of Mobile Search Functions of Korean Search Portals." Journal of the Korean Society for information Management 29, no. 1 (March 30, 2012): 175–90. http://dx.doi.org/10.3743/kosim.2012.29.1.175.

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10

N., Angel. "Hybrid Genetic Gravitational Search Algorithm for Energy Efficient Trust Node Identification in Mobile Adhoc Networks." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (February 28, 2020): 74–86. http://dx.doi.org/10.5373/jardcs/v12sp3/20201240.

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11

Hu, Mingming, Mengqing Xiao, and Hengyun Li. "Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC?" International Journal of Contemporary Hospitality Management 33, no. 6 (May 31, 2021): 2022–43. http://dx.doi.org/10.1108/ijchm-06-2020-0559.

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Purpose While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting. Design/methodology/approach Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting. Findings Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal. Practical implications Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management. Originality/value This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.

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12

Jones, Matt. "Classic and Alternative Mobile Search." International Journal of Mobile Human Computer Interaction 3, no. 1 (January 2011): 22–36. http://dx.doi.org/10.4018/jmhci.2011010102.

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As mobile search turns into a mainstream activity, the author reflects on research that provides insights into the impact of current interfaces and pointers to yet unmet needs. Classic text dominated interface and interaction techniques are reviewed, showing how they can enhance the user experience. While today’s interfaces emphasise direct, query-result approaches, serving up discrete chunks of content, the author suggests an alternative set of features for future mobile search. With reference to example systems, the paper argues for indirect, continuous and multimodal approaches. Further, while almost all mobile search research has focused on the ‘developed’ world, the paper outlines challenges and impact of work targeted at ‘developing’ world contexts.

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13

ZHANG, Jin-Zeng, and Xiao-Feng MENG. "Research on Mobile Web Search." Journal of Software 23, no. 1 (March 5, 2012): 46–64. http://dx.doi.org/10.3724/sp.j.1001.2012.04120.

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14

Strickland, Eliza. "Chinese search giant goes mobile." IEEE Spectrum 49, no. 8 (August 2012): 11–12. http://dx.doi.org/10.1109/mspec.2012.6247545.

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15

Xing Xie, Lie Lu, Menglei Jia, Hua Li, F. Seide, and Wei-Ying Ma. "Mobile Search With Multimodal Queries." Proceedings of the IEEE 96, no. 4 (April 2008): 589–601. http://dx.doi.org/10.1109/jproc.2008.916351.

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16

Peng, Peng, Jianqiao Li, and Ze-Nian Li. "Quality-aware Mobile Visual Search." Procedia - Social and Behavioral Sciences 147 (August 2014): 383–89. http://dx.doi.org/10.1016/j.sbspro.2014.07.116.

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17

Janapa Reddi, Vijay, Benjamin C. Lee, Trishul Chilimbi, and Kushagra Vaid. "Web search using mobile cores." ACM SIGARCH Computer Architecture News 38, no. 3 (June 19, 2010): 314–25. http://dx.doi.org/10.1145/1816038.1816002.

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18

Gupta, Disha, and Nekita Chavhan. "Personalized Mobile Web Search Techniques." International Journal of Scientific and Engineering Research 4, no. 11 (November 20, 2014): 1193–98. http://dx.doi.org/10.14299/ijser.2013.11.001.

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19

Kamvar, Maryam, and Shumeet Baluja. "Deciphering Trends in Mobile Search." Computer 40, no. 8 (August 2007): 58–62. http://dx.doi.org/10.1109/mc.2007.270.

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20

Datta, Anindya, Sangaralingam Kajanan, and Nargis Pervin. "A Mobile App Search Engine." Mobile Networks and Applications 18, no. 1 (October 11, 2012): 42–59. http://dx.doi.org/10.1007/s11036-012-0413-z.

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21

Shin, Sangjin, Jihoon Ko, Sungkwang Eom, Minjae Song, Dong-Hoon Shin, and Kyong-Ho Lee. "Keyword-based mobile semantic search using mobile ontology." Journal of Information Science 41, no. 2 (December 22, 2014): 178–96. http://dx.doi.org/10.1177/0165551514560669.

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22

Chun, Ji Young, and Geontae Noh. "Privacy-Preserving RFID-Based Search System." Electronics 10, no. 5 (March 4, 2021): 599. http://dx.doi.org/10.3390/electronics10050599.

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The employment of mobile readers (or mobile phone collaborated with a Radio frequency identification (RFID) reader) opens a novel application for RFID technology. In particular, an RFID tag search system has been designed to find a particular tag in a group of tags using a mobile reader. Unfortunately, privacy infringement and availability issues in the search system have not been adequately addressed to date. In this paper, we propose a novel RFID tag search protocol that will enhance mobile reader user privacy while being able to operate under conditions of unstable connection to a central server. First, the proposed protocol preserves the privacy of mobile reader users. The privacy of the mobile reader user is at risk because the signal strength emitted from a mobile reader is much stronger than that from the tag, exposing the location of the mobile reader user and thus compromising the user’s privacy. Thus far, such privacy issues have been overlooked. The second issue is presented because of wireless connections that are either unreliable or too remote, causing a mobile reader to disconnect from the central server. The proposed protocol enables serverless RFID tag searches with passive tags, which obtain operating power from the mobile reader. In unstable environments, the protocol can successfully locate specific tags without any server.

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23

Ge, Wei, Cheng De Qin, and Xiao Hong Qin. "Comparative Study on Mobile Search Engines." Applied Mechanics and Materials 533 (February 2014): 436–39. http://dx.doi.org/10.4028/www.scientific.net/amm.533.436.

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On the last day of 2008, China finally agreed to three grant telecom operators licenses to offer the third-generation (3G) services using WCDMA, CDMA 2000, and TD-SCDMA standards. The Issuing of 3G Licenses is going to excite the mobile business, and also expand the broad user basis and development space of the mobile search industry. Today mobile search has become one of the hot topics in the mobile field and the search field, and many giant companies have started action.

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24

Schubert, David. "Influence of Mobile-friendly Design to Search Results on Google Search." Procedia - Social and Behavioral Sciences 220 (May 2016): 424–33. http://dx.doi.org/10.1016/j.sbspro.2016.05.517.

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25

ZHU, Chuang, Xiao Feng HUANG, Guo Qing XIANG, Hui Hui DONG, and Jia Wen SONG. "Highly Efficient Mobile Visual Search Algorithm." IEICE Transactions on Information and Systems E101.D, no. 12 (December 1, 2018): 3073–82. http://dx.doi.org/10.1587/transinf.2018edp7075.

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26

Westlund, Oscar, José-Luis Gómez-Barroso, Ramón Compañó, and Claudio Feijóo. "Exploring the logic of mobile search." Behaviour & Information Technology 30, no. 5 (September 2011): 691–703. http://dx.doi.org/10.1080/0144929x.2010.516020.

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27

Gomez-Barroso, Jose Luis, Claudio Feijoo, and Ramon Compano. "Opportunities in the Mobile Search Market." Computer 44, no. 11 (November 2011): 83–85. http://dx.doi.org/10.1109/mc.2011.347.

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28

Leung, Kenneth Wai-Ting, Dik Lun Lee, and Wang-Chien Lee. "PMSE: A Personalized Mobile Search Engine." IEEE Transactions on Knowledge and Data Engineering 25, no. 4 (April 2013): 820–34. http://dx.doi.org/10.1109/tkde.2012.23.

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29

Dewri, Rinku, and Ramakrishna Thurimella. "Mobile local search with noisy locations." Pervasive and Mobile Computing 32 (October 2016): 78–92. http://dx.doi.org/10.1016/j.pmcj.2016.04.014.

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30

Chen, Wen-Shyen E., Chun-Wu R. Leng, and Yao-Nan Lien. "A novel mobile agent search algorithm." Information Sciences 122, no. 2-4 (February 2000): 227–40. http://dx.doi.org/10.1016/s0020-0255(99)00122-x.

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31

Yaw, Amoako-Frimpong Samuel, Matthew Messina, Henry Medeiros, Jeremy Marvel, and Roger Bostelman. "Stochastic Search Methods for Mobile Manipulators." Procedia Manufacturing 17 (2018): 976–84. http://dx.doi.org/10.1016/j.promfg.2018.10.106.

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32

Church, Karen, and Barry Smyth. "Improving mobile search using content enrichment." Artificial Intelligence Review 28, no. 1 (June 2007): 87–102. http://dx.doi.org/10.1007/s10462-008-9073-6.

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33

Sang, Jitao, Tao Mei, Ying-Qing Xu, Chen Zhao, Changsheng Xu, and Shipeng Li. "Interaction Design for Mobile Visual Search." IEEE Transactions on Multimedia 15, no. 7 (November 2013): 1665–76. http://dx.doi.org/10.1109/tmm.2013.2268052.

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34

Min, Weiqing, Changsheng Xu, Min Xu, Xian Xiao, and Bing-Kun Bao. "Mobile Landmark Search with 3D Models." IEEE Transactions on Multimedia 16, no. 3 (April 2014): 623–36. http://dx.doi.org/10.1109/tmm.2014.2302744.

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35

Arase, Yuki, Takahiro Hara, Daijiro Komaki, and Shojiro Nishio. "Mobile search assistance from HCI aspect." International Journal of Space-Based and Situated Computing 1, no. 1 (2011): 18. http://dx.doi.org/10.1504/ijssc.2011.039104.

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36

Çalışır, Fatih, Muhammet Baştan, Özgür Ulusoy, and Uğur Güdükbay. "Mobile multi-view object image search." Multimedia Tools and Applications 76, no. 10 (June 10, 2016): 12433–56. http://dx.doi.org/10.1007/s11042-016-3659-9.

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37

Broussard, Ramona, Yongyi Zhou, and Matthew Lease. "Mobile phone search for library catalogs." Proceedings of the American Society for Information Science and Technology 47, no. 1 (November 2010): 1–4. http://dx.doi.org/10.1002/meet.14504701128.

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38

Broussard, Ramona, Yongyi Zhou, and Matthew Lease. "University of Texas mobile library search." Proceedings of the American Society for Information Science and Technology 47, no. 1 (November 2010): 1–2. http://dx.doi.org/10.1002/meet.14504701385.

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39

Wu, Dan, Man Zhu, and Aihua Ran. "How Users Search the Mobile Web: A Model for Understanding the Impact of Motivation and Context on Search Behaviors." Journal of Data and Information Science 1, no. 1 (September 1, 2017): 98–122. http://dx.doi.org/10.20309/jdis.201608.

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Анотація:

AbstractPurposeThis study explores how search motivation and context influence mobile Web search behaviors.Design/methodology/approachWe studied 30 experienced mobile Web users via questionnaires, semi-structured interviews, and an online diary tool that participants used to record their daily search activities. SQLite Developer was used to extract data from the users’ phone logs for correlation analysis in Statistical Product and Service Solutions (SPSS).FindingsOne quarter of mobile search sessions were driven by two or more search motivations. It was especially difficult to distinguish curiosity from time killing in particular user reporting. Multi-dimensional contexts and motivations influenced mobile search behaviors, and among the context dimensions, gender, place, activities they engaged in while searching, task importance, portal, and interpersonal relations (whether accompanied or alone when searching) correlated with each other.Research limitationsThe sample was comprised entirely of college students, so our findings may not generalize to other populations. More participants and longer experimental duration will improve the accuracy and objectivity of the research.Practical implicationsMotivation analysis and search context recognition can help mobile service providers design applications and services for particular mobile contexts and usages.Originality/valueMost current research focuses on specific contexts, such as studies on place, or other contextual influences on mobile search, and lacks a systematic analysis of mobile search context. Based on analysis of the impact of mobile search motivations and search context on search behaviors, we built a multi-dimensional model of mobile search behaviors.

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40

Wu, Dan, Rui Qiao, and Yi Li. "A study on location-based mobile map search behavior." Program 50, no. 3 (July 4, 2016): 246–69. http://dx.doi.org/10.1108/prog-11-2015-0074.

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Purpose – Mobile users increasingly employ location-based map searches in their daily lives. However, it is still relatively unknown about mobile users’ map related search behaviors. The purpose of this paper is to discover the interactions between the users and mobile map search systems, to reveal the shortcomings of existing mobile map search functions, and to propose improvement suggestions. Design/methodology/approach – Based on a set of controlled user experiments performed on the Baidu mobile phone map, this paper empirically examines users’ location-based mobile search behaviors, such as timing, metering, judging and so on. This paper also conducts statistical correlation tests to generate relation tables and diagrams regarding each variable, for example, the relation between the retrieval time and the retrieval steps. Findings – The results indicate that mobile map users have two important characteristics in their search behaviors: first, mobile map users always follow the single search path. Second, the mobile map search efficiency of users is always low. Research limitations/implications – The situation simulation testing method is mainly used for the construction of a mobile information search behavior environment, which may make the users be nervous and have some effect on the search efficiency. Practical implications – Based on the identification of user behaviors, this paper provides suggestions to optimize and improve mobile map search systems. Originality/value – This paper studies users’ mobile map search behavior based on location and explores the features of user behavior from the perspective of human-computer interaction.

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41

Liang, Shaobo, Dan Wu, and Jing Dong. "Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches." Sustainability 14, no. 20 (October 11, 2022): 12992. http://dx.doi.org/10.3390/su142012992.

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Анотація:

Mobile searches have become the main channel for people to search for information, and mobile searches have received attention in the field of information-seeking behavior. Especially as users use various apps to search network information, the app-switching behaviors in mobile searches have also attracted scholars’ attention in recent years. Research on app-switching behaviors in mobile searches can help to further understand users’ search motivations, evaluate search results, and improve users’ mobile search experiences. This study recruited participants (n = 30) and conducted a 15-day user experiment. This study collected all participants’ mobile phone log data during the experiment and identified the app-switching behaviors in mobile searches through a log collection tool. This study aimed to discover the app-switching behavior paths and patterns in mobile searches. Firstly, the basic characteristics of app-switching behaviors in mobile searches were analyzed, as were the app-switching paths in mobile searches from the perspective of switching probability between apps. Then, the different behaviors in mobile search sessions were identified and app-switching behavior patterns were put forward. These behavior patterns summarize user behavior changes in mobile search sessions. This paper focused on analyzing app-switching behavior paths under different patterns and found apparent differences in app-switching behavior paths. This study examined mobile search behavior from the perspective of app-switching. The research of this paper can help to better understand the relationship between users’ mobile search behaviors and app interactions and is an excellent supplement to the analysis of mobile search behaviors.

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42

Chumtong, Puwanan, Yasushi Mae, Kenichi Ohara, Tomohito Takubo, and Tatsuo Arai. "1P1-M06 Autonomous Object Search in 3D Environment Using Object Co-occurrence Graph(Vision System for Mobile Robot)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _1P1—M06_1—_1P1—M06_4. http://dx.doi.org/10.1299/jsmermd.2011._1p1-m06_1.

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43

Lee, Sungin, Wonhong Jang, Eunsol Lee, and Sam G. Oh. "Search engine optimization." Library Hi Tech 34, no. 2 (June 20, 2016): 197–206. http://dx.doi.org/10.1108/lht-02-2016-0014.

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Анотація:

Purpose – The purpose of this paper is to examine the effect of, and identify core techniques of, search engine optimization (SEO) techniques applied to the web (http://lg-sl.net) and mobile (http//m.lg-sl.net) Science Land content and services at LG Sangnam Library in Korea. Design/methodology/approach – In accordance with three major SEO guidelines, ten SEO techniques were identified and applied, and their implications were extracted on three areas: improved search engine accessibility, increased relevance between site content and search engine keywords, and improved site credibility. The effects were quantitatively analyzed in terms of registered search engine keywords and influx of visits via search engines. Findings – This study shows that SEO techniques help increase the exposure of the library services and the number of visitors through search engines. Practical implications – SEO techniques have been applied to a few non-Korean information service organizations, but it is not a well-accepted practice in Korean libraries. And the dominant search engines in Korea have published their own SEO guidelines. Prior to this study, no significant endeavors have been undertaken in the context of Korean library services that have adopted SEO techniques to boost exposure of library services and increase user traffics. Originality/value – This is the first published study that has applied optimized SEO techniques to Korean web and mobile library services, in order to demonstrate the usefulness of the techniques for maximized exposure of library content.

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Li, Jianxin, Chengfei Liu, and Jiajie Xu. "XBridge-Mobile: efficient XML keyword search on mobile web data." Computing 96, no. 7 (March 6, 2013): 631–50. http://dx.doi.org/10.1007/s00607-013-0315-3.

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Liang, Shaobo. "Characterizing and predicting the cross-app behavior in mobile search." Aslib Journal of Information Management 74, no. 1 (October 15, 2021): 78–93. http://dx.doi.org/10.1108/ajim-08-2021-0220.

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PurposeThis paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.Design/methodology/approachThis paper presents a longitudinal user experiment in 15 days. This paper recruited 30 participants and collected their mobile phone log data in the whole experiment. The structured diary method was also used to collect contextual information in mobile search.FindingsThis study focused on the users' cross-app behavior in mobile search and described cross-app behavior's basic characteristics. Usage of communication app and tool apps could trigger more cross-app behavior in mobile search. The method of cross-app behavior prediction in the mobile search was proposed. Collecting users' more contextual information, such as search tasks, search motivation and other environmental information, can effectively improve the prediction accuracy of cross-app behavior in mobile search.Practical implicationsThe future research on cross-app behavior prediction should focus on context information in mobile search. Better prediction of cross-app behavior can reduce the users' interaction burden.Originality/valueThis paper contributes to research into cross-app behavior, especially in the mobile search research domain.

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Keyaki, Atsushi, Jun Miyazaki, and Kenji Hatano. "Effective Mobile Search Using Element-based Retrieval." Journal of Information Processing 25 (2017): 934–44. http://dx.doi.org/10.2197/ipsjjip.25.934.

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47

Luis Gómez‐Barroso, José, Margherita Bacigalupo, Stavri G. Nikolov, Ramón Compañó, and Claudio Feijóo. "Factors required for mobile search going mainstream." Online Information Review 36, no. 6 (November 23, 2012): 846–57. http://dx.doi.org/10.1108/14684521211287918.

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48

Kim. "Intelligent Semantic Universal Search in Mobile Platform." Journal of Computer Science 8, no. 5 (May 1, 2012): 796–803. http://dx.doi.org/10.3844/jcssp.2012.796.803.

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Kremer and Gerrit. "Search for tools using the mobile application." Glavnyj mekhanik (Chief Mechanic), no. 12 (December 1, 2020): 39–41. http://dx.doi.org/10.33920/pro-2-2012-04.

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This article provides information about the program released by Seco Tools. The Seco Assistant mobile app provides all the necessary data about the products and tools without having to search the catalog, even without an Internet access. The application with a simple interface is designed with an emphasis on maximum usability and takes up a small amount of phone memory. The presented program will be useful for the chief mechanic's services and will reduce the search for the necessary tools.

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White, Ryen W., Fernando Diaz, and Qi Guo. "Search Result Prefetching on Desktop and Mobile." ACM Transactions on Information Systems 35, no. 3 (June 9, 2017): 1–34. http://dx.doi.org/10.1145/3015466.

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