Relevant bibliographies by topics / Key discovery

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Key discovery'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Key discovery"

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Kamel, Mohammed B. M., Yuping Yan, Peter Ligeti, and Christoph Reich. "Attred: Attribute Based Resource Discovery for IoT." Sensors 21, no. 14 (July 10, 2021): 4721. http://dx.doi.org/10.3390/s21144721.

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While the number of devices connected together as the Internet of Things (IoT) is growing, the demand for an efficient and secure model of resource discovery in IoT is increasing. An efficient resource discovery model distributes the registration and discovery workload among many nodes and allow the resources to be discovered based on their attributes. In most cases this discovery ability should be restricted to a number of clients based on their attributes, otherwise, any client in the system can discover any registered resource. In a binary discovery policy, any client with the shared secret key can discover and decrypt the address data of a registered resource regardless of the attributes of the client. In this paper we propose Attred, a decentralized resource discovery model using the Region-based Distributed Hash Table (RDHT) that allows secure and location-aware discovery of the resources in IoT network. Using Attribute Based Encryption (ABE) and based on predefined discovery policies by the resources, Attred allows clients only by their inherent attributes, to discover the resources in the network. Attred distributes the workload of key generations and resource registration and reduces the risk of central authority management. In addition, some of the heavy computations in our proposed model can be securely distributed using secret sharing that allows a more efficient resource registration, without affecting the required security properties. The performance analysis results showed that the distributed computation can significantly reduce the computation cost while maintaining the functionality. The performance and security analysis results also showed that our model can efficiently provide the required security properties of discovery correctness, soundness, resource privacy and client privacy.
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Celis, Julio E. "Proteomics: key technology in drug discovery." Drug Discovery Today 3, no. 5 (May 1998): 193–95. http://dx.doi.org/10.1016/s1359-6446(98)01184-2.

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Zhang, Meihui, Marios Hadjieleftheriou, Beng Chin Ooi, Cecilia M. Procopiuc, and Divesh Srivastava. "On multi-column foreign key discovery." Proceedings of the VLDB Endowment 3, no. 1-2 (September 2010): 805–14. http://dx.doi.org/10.14778/1920841.1920944.

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Toga, Arthur W., Ian Foster, Carl Kesselman, Ravi Madduri, Kyle Chard, Eric W. Deutsch, Nathan D. Price, et al. "Big biomedical data as the key resource for discovery science." Journal of the American Medical Informatics Association 22, no. 6 (July 21, 2015): 1126–31. http://dx.doi.org/10.1093/jamia/ocv077.

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Abstract Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an “-ome to home” approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center’s computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson’s and Alzheimer’s.
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Brown, Helen, David Butler, and Mari Riess Jones. "Musical and Temporal Influences on Key Discovery." Music Perception 11, no. 4 (1994): 371–407. http://dx.doi.org/10.2307/40285632.

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The intervallic rivalry model of key identification is outlined and evaluated in two experiments that use a completion judgment task. Experiment 1 replicates an earlier experiment by Cuddy and Badertscher (1987), in which the rare-interval hypothesis of the intervallic rivalry model was considered. In the present study, listeners with different levels of musical training rated probe tones in the context of three different melodic patterns: arpeggiated major triads, ascending major scales, and arpeggiated diminished triads. Results of Experiment 1 indicated that in both the C major triadic and the C major scalar contexts, listeners gave higher completion ratings to all three probes that were members of the presented C major triad than to the other probes, with the exception of F, thereby producing a jagged (multipeaked) profile. For the diminished triadic context, listeners rated the single probe C, that which corresponds to the tonal center in major mode for that group of three tones, as the best completion. Experiment 2 tested the temporal-order hypothesis of the intervallic rivalry model by reordering tones in all three contexts. Again jagged tone profiles appeared with major triadic and major scalar contexts, although in the former the tone F, a perfect fifth below the root of the presented C major triad, received the best completion rating. A single-peaked function appeared with probes in the diminished triadic context, where the major-mode tonic garnered the highest rating found in all conditions of both experiments. Data are interpreted as support for both the rare-interval hypothesis and the temporal-order hypothesis derived from the intervallic rivalry model of key discovery. Complementary findings consistent with the tonal hierarchy model are also discussed.
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Cheng, Xiaoming, and Marc G. Ghany. "Key Milestones in HCV Discovery and Therapeutics." Innovation 1, no. 3 (November 2020): 100067. http://dx.doi.org/10.1016/j.xinn.2020.100067.

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Ezzell, C. "Gene Discovery: Key to Colon Cancer Test." Science News 140, no. 6 (August 10, 1991): 86. http://dx.doi.org/10.2307/3975961.

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Huang, Ying, Liyun Zhong, and Yan Chen. "Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 1–15. http://dx.doi.org/10.4018/ijcini.2020040101.

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The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process discovery procedure. This article presents a technique to remove infrequent behavior from event logs by calculating the minimum expectation of the process event log. The method was evaluated in detail, and the results showed that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.
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van Rensburg, Ilana C., and André G. Loxton. "Transcriptomics: the key to biomarker discovery during tuberculosis?" Biomarkers in Medicine 9, no. 5 (May 2015): 483–95. http://dx.doi.org/10.2217/bmm.15.16.

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Pernelle, Nathalie, Fatiha Saïs, and Danai Symeonidou. "An automatic key discovery approach for data linking." Journal of Web Semantics 23 (December 2013): 16–30. http://dx.doi.org/10.1016/j.websem.2013.07.001.

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Dissertations / Theses on the topic "Key discovery"

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Symeonidou, Danai. "Automatic key discovery for Data Linking." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112265/document.

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Dans les dernières années, le Web de données a connu une croissance fulgurante arrivant à un grand nombre des triples RDF. Un des objectifs les plus importants des applications RDF est l’intégration de données décrites dans les différents jeux de données RDF et la création des liens sémantiques entre eux. Ces liens expriment des correspondances sémantiques entre les entités d’ontologies ou entre les données. Parmi les différents types de liens sémantiques qui peuvent être établis, les liens d’identité expriment le fait que différentes ressources réfèrent au même objet du monde réel. Le nombre de liens d’identité déclaré reste souvent faible si on le compare au volume des données disponibles. Plusieurs approches de liage de données déduisent des liens d’identité en utilisant des clés. Une clé représente un ensemble de propriétés qui identifie de façon unique chaque ressource décrite par les données. Néanmoins, dans la plupart des jeux de données publiés sur le Web, les clés ne sont pas disponibles et leur déclaration peut être difficile, même pour un expert.L’objectif de cette thèse est d’étudier le problème de la découverte automatique de clés dans des sources de données RDF et de proposer de nouvelles approches efficaces pour résoudre ce problème. Les données publiées sur le Web sont général volumineuses, incomplètes, et peuvent contenir des informations erronées ou des doublons. Aussi, nous nous sommes focalisés sur la définition d’approches capables de découvrir des clés dans de tels jeux de données. Par conséquent, nous nous focalisons sur le développement d’approches de découverte de clés capables de gérer des jeux de données contenant des informations nombreuses, incomplètes ou erronées. Notre objectif est de découvrir autant de clés que possible, même celles qui sont valides uniquement dans des sous-ensembles de données.Nous introduisons tout d’abord KD2R, une approche qui permet la découverte automatique de clés composites dans des jeux de données RDF pour lesquels l’hypothèse du nom Unique est respectée. Ces données peuvent être conformées à des ontologies différentes. Pour faire face à l’incomplétude des données, KD2R propose deux heuristiques qui per- mettent de faire des hypothèses différentes sur les informations éventuellement absentes. Cependant, cette approche est difficilement applicable pour des sources de données de grande taille. Aussi, nous avons développé une seconde approche, SAKey, qui exploite différentes techniques de filtrage et d’élagage. De plus, SAKey permet à l’utilisateur de découvrir des clés dans des jeux de données qui contiennent des données erronées ou des doublons. Plus précisément, SAKey découvre des clés, appelées "almost keys", pour lesquelles un nombre d’exceptions est toléré
In the recent years, the Web of Data has increased significantly, containing a huge number of RDF triples. Integrating data described in different RDF datasets and creating semantic links among them, has become one of the most important goals of RDF applications. These links express semantic correspondences between ontology entities or data. Among the different kinds of semantic links that can be established, identity links express that different resources refer to the same real world entity. By comparing the number of resources published on the Web with the number of identity links, one can observe that the goal of building a Web of data is still not accomplished. Several data linking approaches infer identity links using keys. Nevertheless, in most datasets published on the Web, the keys are not available and it can be difficult, even for an expert, to declare them.The aim of this thesis is to study the problem of automatic key discovery in RDF data and to propose new efficient approaches to tackle this problem. Data published on the Web are usually created automatically, thus may contain erroneous information, duplicates or may be incomplete. Therefore, we focus on developing key discovery approaches that can handle datasets with numerous, incomplete or erroneous information. Our objective is to discover as many keys as possible, even ones that are valid in subparts of the data.We first introduce KD2R, an approach that allows the automatic discovery of composite keys in RDF datasets that may conform to different schemas. KD2R is able to treat datasets that may be incomplete and for which the Unique Name Assumption is fulfilled. To deal with the incompleteness of data, KD2R proposes two heuristics that offer different interpretations for the absence of data. KD2R uses pruning techniques to reduce the search space. However, this approach is overwhelmed by the huge amount of data found on the Web. Thus, we present our second approach, SAKey, which is able to scale in very large datasets by using effective filtering and pruning techniques. Moreover, SAKey is capable of discovering keys in datasets where erroneous data or duplicates may exist. More precisely, the notion of almost keys is proposed to describe sets of properties that are not keys due to few exceptions
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Maartmann-Moe, Carsten. "Forensic Key Discovery and Identification : Finding Cryptographic Keys in Physical Memory." Thesis, Norwegian University of Science and Technology, Department of Telematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8895.

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Communication and whole-disk cryptosystems are on the verge of becoming mainstream tools for protection of data, both in corporate laptops and private computing equipment. While encryption is a useful tool, it also present new problems for forensic investigators, as clues to their investigation may be undecipherable. However, contrary to popular belief, these systems are not impenetrable. Forensic memory dumping and analysis can pose as ways to recover cryptographic keys that are present in memory due to bad coding practice, operation system quirks or hardware hacks. The volatile nature of physical memory does however challenge the classical principles of digital forensics as its transitory state may disappear at the flick of a switch. In this thesis, we analyze existing and present new cryptographic key search algorithms, together with different confiscation and analysis methods for images of volatile memory. We provide a new proof of concept tool that can analyze memory images and recover cryptographic keys, and use this tool together with a virtualized testbed to simulate and examine the different states of platforms with several separate cryptosystems. Making use of this testbed, we provide experiments to point out how modern day encryption in general are vulnerable to memory disclosure attacks. We show that memory management procedures, coding practice and the overall state of the system has great impact on the amount and quality of data that can be extracted, and present simple statistics of our findings. The discoveries have significant implications for most software encryption vendors and the businesses relying on these for data security. Using our results, we suggest best practices that can help investigators build a more comprehensive data foundation for analysis, by reconstructing virtual memory from RAM images. We also discuss how investigators may reduce the haystack by leveraging memory and process structure on Windows computers. Finally we tie this to current digital forensic procedures, and suggest an optimized way of handling live analysis based on the latest development in the field.

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Abedjan, Ziawasch, and Felix Naumann. "Advancing the discovery of unique column combinations." Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5356/.

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Unique column combinations of a relational database table are sets of columns that contain only unique values. Discovering such combinations is a fundamental research problem and has many different data management and knowledge discovery applications. Existing discovery algorithms are either brute force or have a high memory load and can thus be applied only to small datasets or samples. In this paper, the wellknown GORDIAN algorithm and "Apriori-based" algorithms are compared and analyzed for further optimization. We greatly improve the Apriori algorithms through efficient candidate generation and statistics-based pruning methods. A hybrid solution HCAGORDIAN combines the advantages of GORDIAN and our new algorithm HCA, and it significantly outperforms all previous work in many situations.
Unique-Spaltenkombinationen sind Spaltenkombinationen einer Datenbanktabelle, die nur einzigartige Werte beinhalten. Das Finden von Unique-Spaltenkombinationen spielt sowohl eine wichtige Rolle im Bereich der Grundlagenforschung von Informationssystemen als auch in Anwendungsgebieten wie dem Datenmanagement und der Erkenntnisgewinnung aus Datenbeständen. Vorhandene Algorithmen, die dieses Problem angehen, sind entweder Brute-Force oder benötigen zu viel Hauptspeicher. Deshalb können diese Algorithmen nur auf kleine Datenmengen angewendet werden. In dieser Arbeit werden der bekannte GORDIAN-Algorithmus und Apriori-basierte Algorithmen zum Zwecke weiterer Optimierung analysiert. Wir verbessern die Apriori Algorithmen durch eine effiziente Kandidatengenerierung und Heuristikbasierten Kandidatenfilter. Eine Hybride Lösung, HCA-GORDIAN, kombiniert die Vorteile von GORDIAN und unserem neuen Algorithmus HCA, welche die bisherigen Algorithmen hinsichtlich der Effizienz in vielen Situationen übertrifft.
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Gao, Geng. "Fragment Library Screening to Discover Selective Inhibitors of a Key Microbial Enzyme." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1290184812.

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Gajos, Krzysztof, and Howard Shrobe. "Delegation, Arbitration and High-Level Service Discovery as Key Elements of a Software Infrastructure for Pervasive Computing." 2003. http://hdl.handle.net/1721.1/6721.

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The dream of pervasive computing is slowly becoming a reality. A number of projects around the world are constantly contributing ideas and solutions that are bound to change the way we interact with our environments and with one another. An essential component of the future is a software infrastructure that is capable of supporting interactions on scales ranging from a single physical space to intercontinental collaborations. Such infrastructure must help applications adapt to very diverse environments and must protect people's privacy and respect their personal preferences. In this paper we indicate a number of limitations present in the software infrastructures proposed so far (including our previous work). We then describe the framework for building an infrastructure that satisfies the abovementioned criteria. This framework hinges on the concepts of delegation, arbitration and high-level service discovery. Components of our own implementation of such an infrastructure are presented.
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Chen, Po-Jen, and 陳柏任. "Applying Social Networks Analysis Methods to Discover Key Users in an Interest-Oriented Virtual Community." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/21088919433341653059.

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碩士
國立高雄大學
資訊管理學系碩士班
100
In recent years, with the growth of Internet technology and virtual community, the uses of virtual community not only play as the information receiver but also very important role to provide information. However, information overload has becoming a very serious problem and how to find information efficiently is also an important issue. In this research, we believe that users in a virtual community may affect each other, especially those with high influence. Therefore, we observe the biggest virtual community of movies named IMDb(The Internet Movie Database),and we propose an architecture combine with the Social Network analysis and the features(Useful-index) of the IMDb to discover those users who has high influence in a virtual community. We collected the data of 19 months (2010/1 to 2011/5) , about 17,366 users and 243,074 reviews. By applying the method we proposed, finally about 22 key users and 111 reviews were discovered. We use the box office number to certify our results, and simulated many of different situations to observe the react from our system.
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LEE, MEI-JUNG, and 李美蓉. "From the Viewpoint of Organization Change to Discover the Key Success Factors of InternalControl Process Reengineering." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/00211228663075382076.

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Chang, Wei-Hua, and 張威華. "From the Viewpoint of Organization Change to Discover the Key Success Factors of Enterprise Business Process Management." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/95451720822416493940.

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碩士
國立臺灣科技大學
資訊管理系
97
For growth and business continuity, the enterprise looks for various ways to improve its survival condition and competition advantage. Since Howard Smith and Peter Fingar published “Business Process Management: The Third Wave ”on 2003 and raised a fever of Business Process Management(BPM), because of IT vendor’s promotion, lots of companies implement diverse Business Process Management System and this implementation suddenly becomes the synonym of BPM activity. But, this thinking neglects that the essence of BPM is actually an organization change, in order to make BPM project successful, it is necessary to control the factors and resistances that will affect BPM project’s success. So this research adopts a viewpoint form organization change and makes the conclusion through the unstructured interview with selected cases to discover the resistances and success factors during each steps of BPM project. The derivative model of this research which combines organization change theory with BPM methodology can be adopted as a methodology reference for the enterprise which wants to start a BPM project, and the derivative conclusion from this research that includes: the leader’s will, the support from high level executive, the leader’s authority, the participation of key departments and IT department, the strategy of starting from obvious problem, effective communication, developing the solution with its evaluation method, adopting prototyping methodology and concrete action provides the suggestions for the enterprise to control the success factors of a BPM project.
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Tung, Yu-Ching, and 董伃菁. "Applying Data Mining Approach to Discover the Critical Successful Factors and Key Performance Indicators in PKI Application Deployment." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/91097559103233018093.

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碩士
元智大學
資訊管理學系
94
Use e-business in order to deal in the environment of information science and technology making fast progress and change of the main shaft in enterprises. Hense, the interpersonal trust can''t already only maintained by the agreement of one paper, and when dispute in trade, enterprise''s financial affairs or credit loss brought will be great and difficult to estimate to arrive. So, the enterprises which are based on information system gradually have the idea of PKI. This idea about PKI is one of the agreement of the network environment which utilizes electronic restrain of law (digital signature and certificate authority), and make the agreement carried on by members who exist in every site of the network are, and it could be written down immediately, make the transaction be reached to the superior grade of security, therefore, only people who transact with each other could authorize the transactional behavior, and they have to take the responsibility about the behavior afterwards; the more important things are, such structure can reduce the hacker''s destruction, make the rights of individual or enterprises receive the tight guarantee even more. The e-businesses in Taiwan have great scale and stable operations so far, and they are not only possessed good reputation among homogeneity enterprises but also attract a large number of enterprises to cooperate with each other among various enterprises, so these enterprises may have beneficial terms to develop PKI, in order to create the higher tangible and invisible benefits. In this research, we explore the cases about B2B PKI of Taiwan in the past, and utilized various mature statistical analysis method to analyze the representative factors which conclude CSFs and KPIs; therefore, we utilized the predictive method of data mining field to find out rational models to explain that the implementation about PKI of enterprises could be successful or not could be, and we attempt to offer the models, which could be used by the enterprises that attempt to carry out the PKI, which is refer to the incators and successful model in the future.
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Engelmann, Cindy. "The future of online Grocery Shopping - an explorative approach to discover current German market dynamics." Master's thesis, 2019. http://hdl.handle.net/10362/69481.

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This Work Project focuses on the online grocery industry with its global market characteristics and the present situation in Germany by analysing data from direct literature research and expert interviews. The Work Project examines consumers’ perceptions of online and offline grocery retail and includes industry insights about main challenges of online food retailing in Germany. As a result, three major industry key success factors are identified, which tip the scale about the future success of online food retailing in Germany. These factors are investment in highly-automated logistics, the gain of consumers trust and the differentiation through a versatile service offering.
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Books on the topic "Key discovery"

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Vilarem, Charlotte. Approximate key and foreign key discovery in relational databases. Ottawa: National Library of Canada, 2002.

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Sakoian, Frances, and Frances Sakoian. Astrological patterns: The key to self-discovery. New York: Perennial Library, 1989.

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Seymour, Donald E. The key to your unknown talent: A new discovery about you! Milwaukee, Wis: Talent Discovery Press, 1996.

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KBYU-TV (Television station : Provo, Utah), ed. Ancestors, guide to discovery: Key principles and processes of family history research. [Salt Lake Valley, UT]: Ancestral Quest, 2000.

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Bill, Gottlieb, ed. The calcium key: The revolutionary diet discovery that will help you lose weight faster. Hoboken, N.J: John Wiley & Sons, 2004.

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1947-, Lomas Robert, ed. The Hiram key: Pharaohs, Freemasons, and the discovery of the secret scrolls of Jesus. New York: Barnes & Noble, 1998.

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1947-, Lomas Robert, ed. The Hiram key: Pharaohs, Freemasons and the discovery of the secret scrolls of Jesus. Gloucester, Mass: Fair Winds Press, 2005.

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1947-, Lomas Robert, ed. The Hiram key: Pharaohs, Freemasons, and the discovery of the secret scrolls of Jesus. Shaftesbury, Dorset: Element, 1998.

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1947-, Lomas Robert, ed. The Hiram key: Pharaohs, Freemasons, and the discovery of the secret scrolls of Jesus. Rockport, Mass: Element, 1997.

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1947-, Lomas Robert, ed. The Hiram key: Pharaohs, Freemasons and the discovery of the secret scrolls of Jesus. London: Arrow Books, 1997.

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Book chapters on the topic "Key discovery"

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Xu, Ye, and Dan Rockmore. "Sparse Coding for Key Node Selection over Networks." In Discovery Science, 337–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11812-3_29.

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Bawa, Raj, Yechezkel Barenholz, and Andrew Owen. "Chapter 12. The Challenge of Regulating Nanomedicine: Key Issues." In Drug Discovery, 290–314. Cambridge: Royal Society of Chemistry, 2016. http://dx.doi.org/10.1039/9781782622536-00290.

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Dovgan, Erik, Bojan Leskošek, Gregor Jurak, Gregor Starc, Maroje Sorić, and Mitja Luštrek. "Enhancing BMI-Based Student Clustering by Considering Fitness as Key Attribute." In Discovery Science, 155–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33778-0_13.

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Seki, Hirohisa, Yuya Honda, and Shinya Nagano. "On Enumerating Frequent Closed Patterns with Key in Multi-relational Data." In Discovery Science, 72–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16184-1_6.

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Kwon, Joon Hee, and Sung Rim Kim. "Context-Aware Recommendation Using Pattern Discovery in Ubiquitous Computing." In Key Engineering Materials, 278–86. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-958-x.278.

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Zhao, Meiyuan, and Sean W. Smith. "Modeling and Evaluation of Certification Path Discovery in the Emerging Global PKI." In Public Key Infrastructure, 16–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11774716_2.

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Park, Jin Ah, Chang Su Lee, and Jong C. Park. "Information Visualization with Text Data Mining for Knowledge Discovery Tools in Bioinformatics." In Key Engineering Materials, 259–65. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-958-x.259.

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Wurtman, R. J. "Some philosophical aspects of Alzheimer’s discovery: an American perspective." In Key Topics in Brain Research, 3–5. Vienna: Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-3396-5_1.

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Bell, Ian M., Mark T. Bilodeau, and Armando A. Lagrutta. "Activity at Cardiovascular Ion Channels: A Key Issue for Drug Discovery." In Polypharmacology in Drug Discovery, 83–109. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118098141.ch5.

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Zhang, Zhenguo, Haiwei Zhang, Yanlong Wen, and Xiaojie Yuan. "Accelerating Time Series Shapelets Discovery with Key Points." In Web Technologies and Applications, 330–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45817-5_26.

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Conference papers on the topic "Key discovery"

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Chen, Yanping, Sha Liu, Qinghua Zheng, Ruizhang Huang, Yongbin Qin, Jiwei Qin, and Ping Chen. "Discovery of Rare Key Phrases." In 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE). IEEE, 2018. http://dx.doi.org/10.1109/icebe.2018.00028.

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Lotz, Paul J. "Discovery Channel Telescope software key technologies." In SPIE Astronomical Telescopes + Instrumentation, edited by Nicole M. Radziwill and Alan Bridger. SPIE, 2010. http://dx.doi.org/10.1117/12.857557.

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Bornemann, Leon, Tobias Bleifuß, Dmitri V. Kalashnikov, Felix Naumann, and Divesh Srivastava. "Natural Key Discovery in Wikipedia Tables." In WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366423.3380039.

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Leung, Carson K., Peter Braun, Murun Enkhee, Adam G. M. Pazdor, Oluwafemi A. Sarumi, and Kimberly Tran. "Knowledge Discovery from Big Social Key-Value Data." In 2016 IEEE International Conference on Computer and Information Technology (CIT). IEEE, 2016. http://dx.doi.org/10.1109/cit.2016.37.

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Musa, Ahmed, Yahaya Yusuf, Matthias Meckel, and Hamidreza Soltani. "Itxkeys: A robust key parser for LATEX." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234179.

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Bhattacharyya, Bhaswati, Geoffrey Wright, Yashwant Gupta, Patrick Weltevrede, Marta Burgay, Nicolò D’Amico, Paolo Esposito, Alberto Pellizzoni, and Andrea Possenti. "Discovery of phase related drift regions in PSR B1039−19." In RADIO PULSARS: AN ASTROPHYSICAL KEY TO UNLOCK THE SECRETS OF THE UNIVERSE. AIP, 2011. http://dx.doi.org/10.1063/1.3615099.

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Liu, Yang, Zhonglei Gu, Tobey H. Ko, and Jiming Liu. "Brand key asset discovery via cluster-wise biased discriminant projection." In WI '17: International Conference on Web Intelligence 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3106426.3106516.

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Sakurai, Shigeaki, and Akihiro Suyama. "Rule discovery from textual data based on key phrase patterns." In the 2004 ACM symposium. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/967900.968025.

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Ahmmed, Ashek, Aous Thabit Naman, and David Taubman. "Enhanced Homogeneous Motion Discovery Oriented Prediction for Key Intermediate Frames." In 2018 Picture Coding Symposium (PCS). IEEE, 2018. http://dx.doi.org/10.1109/pcs.2018.8456251.

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Huang, Tzu-Heng, Cheng-Hsien Tsai, and Man-Kwan Shan. "Key sensor discovery for quality audit of air sensor networks." In MobiSys '20: The 18th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3386901.3396606.

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Reports on the topic "Key discovery"

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Gagliano, R., S. Krishnan, and A. Kukec. Subject Key Identifier (SKI) SEcure Neighbor Discovery (SEND) Name Type Fields. RFC Editor, February 2012. http://dx.doi.org/10.17487/rfc6495.

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Kempf, J., and R. Koodli. Distributing a Symmetric Fast Mobile IPv6 (FMIPv6) Handover Key Using SEcure Neighbor Discovery (SEND). RFC Editor, June 2008. http://dx.doi.org/10.17487/rfc5269.

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Saville, Alan, and Caroline Wickham-Jones, eds. Palaeolithic and Mesolithic Scotland : Scottish Archaeological Research Framework Panel Report. Society for Antiquaries of Scotland, June 2012. http://dx.doi.org/10.9750/scarf.06.2012.163.

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Why research Palaeolithic and Mesolithic Scotland? Palaeolithic and Mesolithic archaeology sheds light on the first colonisation and subsequent early inhabitation of Scotland. It is a growing and exciting field where increasing Scottish evidence has been given wider significance in the context of European prehistory. It extends over a long period, which saw great changes, including substantial environmental transformations, and the impact of, and societal response to, climate change. The period as a whole provides the foundation for the human occupation of Scotland and is crucial for understanding prehistoric society, both for Scotland and across North-West Europe. Within the Palaeolithic and Mesolithic periods there are considerable opportunities for pioneering research. Individual projects can still have a substantial impact and there remain opportunities for pioneering discoveries including cemeteries, domestic and other structures, stratified sites, and for exploring the huge evidential potential of water-logged and underwater sites. Palaeolithic and Mesolithic archaeology also stimulates and draws upon exciting multi-disciplinary collaborations. Panel Task and Remit The panel remit was to review critically the current state of knowledge and consider promising areas of future research into the earliest prehistory of Scotland. This was undertaken with a view to improved understanding of all aspects of the colonization and inhabitation of the country by peoples practising a wholly hunter-fisher-gatherer way of life prior to the advent of farming. In so doing, it was recognised as particularly important that both environmental data (including vegetation, fauna, sea level, and landscape work) and cultural change during this period be evaluated. The resultant report, outlines the different areas of research in which archaeologists interested in early prehistory work, and highlights the research topics to which they aspire. The report is structured by theme: history of investigation; reconstruction of the environment; the nature of the archaeological record; methodologies for recreating the past; and finally, the lifestyles of past people – the latter representing both a statement of current knowledge and the ultimate aim for archaeologists; the goal of all the former sections. The document is reinforced by material on-line which provides further detail and resources. The Palaeolithic and Mesolithic panel report of ScARF is intended as a resource to be utilised, built upon, and kept updated, hopefully by those it has helped inspire and inform as well as those who follow in their footsteps. Future Research The main recommendations of the panel report can be summarized under four key headings:  Visibility: Due to the considerable length of time over which sites were formed, and the predominant mobility of the population, early prehistoric remains are to be found right across the landscape, although they often survive as ephemeral traces and in low densities. Therefore, all archaeological work should take into account the expectation of Palaeolithic and Mesolithic ScARF Panel Report iv encountering early prehistoric remains. This applies equally to both commercial and research archaeology, and to amateur activity which often makes the initial discovery. This should not be seen as an obstacle, but as a benefit, and not finding such remains should be cause for question. There is no doubt that important evidence of these periods remains unrecognised in private, public, and commercial collections and there is a strong need for backlog evaluation, proper curation and analysis. The inadequate representation of Palaeolithic and Mesolithic information in existing national and local databases must be addressed.  Collaboration: Multi-disciplinary, collaborative, and cross- sector approaches must be encouraged – site prospection, prediction, recognition, and contextualisation are key areas to this end. Reconstructing past environments and their chronological frameworks, and exploring submerged and buried landscapes offer existing examples of fruitful, cross-disciplinary work. Palaeolithic and Mesolithic archaeology has an important place within Quaternary science and the potential for deeply buried remains means that geoarchaeology should have a prominent role.  Innovation: Research-led projects are currently making a substantial impact across all aspects of Palaeolithic and Mesolithic archaeology; a funding policy that acknowledges risk and promotes the innovation that these periods demand should be encouraged. The exploration of lesser known areas, work on different types of site, new approaches to artefacts, and the application of novel methodologies should all be promoted when engaging with the challenges of early prehistory.  Tackling the ‘big questions’: Archaeologists should engage with the big questions of earliest prehistory in Scotland, including the colonisation of new land, how lifestyles in past societies were organized, the effects of and the responses to environmental change, and the transitions to new modes of life. This should be done through a holistic view of the available data, encompassing all the complexities of interpretation and developing competing and testable models. Scottish data can be used to address many of the currently topical research topics in archaeology, and will provide a springboard to a better understanding of early prehistoric life in Scotland and beyond.
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Brophy, Kenny, and Alison Sheridan, eds. Neolithic Scotland: ScARF Panel Report. Society of Antiquaries of Scotland, June 2012. http://dx.doi.org/10.9750/scarf.06.2012.196.

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The main recommendations of the Panel report can be summarised as follows: The Overall Picture: more needs to be understood about the process of acculturation of indigenous communities; about the Atlantic, Breton strand of Neolithisation; about the ‘how and why’ of the spread of Grooved Ware use and its associated practices and traditions; and about reactions to Continental Beaker novelties which appeared from the 25th century. The Detailed Picture: Our understanding of developments in different parts of Scotland is very uneven, with Shetland and the north-west mainland being in particular need of targeted research. Also, here and elsewhere in Scotland, the chronology of developments needs to be clarified, especially as regards developments in the Hebrides. Lifeways and Lifestyles: Research needs to be directed towards filling the substantial gaps in our understanding of: i) subsistence strategies; ii) landscape use (including issues of population size and distribution); iii) environmental change and its consequences – and in particular issues of sea level rise, peat formation and woodland regeneration; and iv) the nature and organisation of the places where people lived; and to track changes over time in all of these. Material Culture and Use of Resources: In addition to fine-tuning our characterisation of material culture and resource use (and its changes over the course of the Neolithic), we need to apply a wider range of analytical approaches in order to discover more about manufacture and use.Some basic questions still need to be addressed (e.g. the chronology of felsite use in Shetland; what kind of pottery was in use, c 3000–2500, in areas where Grooved Ware was not used, etc.) and are outlined in the relevant section of the document. Our knowledge of organic artefacts is very limited, so research in waterlogged contexts is desirable. Identity, Society, Belief Systems: Basic questions about the organisation of society need to be addressed: are we dealing with communities that started out as egalitarian, but (in some regions) became socially differentiated? Can we identify acculturated indigenous people? How much mobility, and what kind of mobility, was there at different times during the Neolithic? And our chronology of certain monument types and key sites (including the Ring of Brodgar, despite its recent excavation) requires to be clarified, especially since we now know that certain types of monument (including Clava cairns) were not built during the Neolithic. The way in which certain types of site (e.g. large palisaded enclosures) were used remains to be clarified. Research and methodological issues: There is still much ignorance of the results of past and current research, so more effective means of dissemination are required. Basic inventory information (e.g. the Scottish Human Remains Database) needs to be compiled, and Canmore and museum database information needs to be updated and expanded – and, where not already available online, placed online, preferably with a Scottish Neolithic e-hub that directs the enquirer to all the available sources of information. The Historic Scotland on-line radiocarbon date inventory needs to be resurrected and kept up to date. Under-used resources, including the rich aerial photography archive in the NMRS, need to have their potential fully exploited. Multi-disciplinary, collaborative research (and the application of GIS modelling to spatial data in order to process the results) is vital if we are to escape from the current ‘silo’ approach and address key research questions from a range of perspectives; and awareness of relevant research outside Scotland is essential if we are to avoid reinventing the wheel. Our perspective needs to encompass multi-scale approaches, so that ScARF Neolithic Panel Report iv developments within Scotland can be understood at a local, regional and wider level. Most importantly, the right questions need to be framed, and the right research strategies need to be developed, in order to extract the maximum amount of information about the Scottish Neolithic.
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