Academic literature on the topic 'Dependency Discovery'

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Journal articles on the topic "Dependency Discovery"

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Papenbrock, Thorsten, Jens Ehrlich, Jannik Marten, et al. "Functional dependency discovery." Proceedings of the VLDB Endowment 8, no. 10 (2015): 1082–93. http://dx.doi.org/10.14778/2794367.2794377.

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Zhao, Anping, Suresh Manandhar, and Lei Yu. "Topology and semantic based topic dependency structure discovery." Filomat 32, no. 5 (2018): 1843–51. http://dx.doi.org/10.2298/fil1805843z.

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As an important enabler in achieving the maximum potential of text data analysis, topic relationship dependency structure discovery is employed to effectively support the advanced text data analysis intelligent application. The proposed framework combines an analysis approach of complex network and the Latent Dirichlet Allocation (LDA) model for topic relationship network discovery. The approach is to identify topics of the text data based on the LDA and to discover the graphical semantic structure of the intrinsic association dependency between topics. This not only exploits the association dependency between topics but also leverages a series of upper-level semantic topics covered by the text data. The results of evaluation and experimental analysis show that the proposed method is effective and feasible. The results of the proposed work imply that the topics and relationships between them can be detected by this approach. It also provides complete semantic interpretation.
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Rajeshwari, T., and C. Thangamani. "Attack Impact Discovery and Recovery with Dynamic Bayesian Networks." Asian Journal of Computer Science and Technology 8, S1 (2019): 74–79. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1953.

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The network attacks are discovered using the Intrusion Detection Systems (IDS). Anomaly, signature and compound attack detection schemes are employed to fetch malicious data traffic activities. The attack impact analysis operations are carried out to discover the malicious objects in the network. The system objects are contaminated with process injection or hijacking. The attack ramification model discovers the contaminated objects. The dependency networks are built to model the information flow over the objects in the network. The dependency network is a directed graph built to indicate the data communication over the objects. The attack ramification models are designed with intrusion root information. The attack ramifications are applied to identify the malicious objects and contaminated objects. The attack ramifications are discovered with the information flows from the attack sources. The Attack Ramification with Bayesian Network (ARBN) scheme discovers the attack impact without the knowledge of the intrusion root. The probabilistic reasoning approach is employed to analyze the object state for ramification process. The objects lifetime is divided into temporal slices to verify the object state changes. The system call traces and object slices are correlated to construct the Temporal Dependency Network (TDN). The Bayesian Network (BN) is constructed with the uncertain data communication activities extracted from the TDN. The attack impact is fetched with loopy belief propagation on the BN model. The network security system is built with attack impact analysis and recovery operations. Live traffic data analysis process is carried out with improved temporal slicing concepts. Attack Ramification and Recovery with Dynamic Bayesian Network (ARRDBN) is built to support attack impact analysis and recovery tasks. The unsupervised attack handling mechanism automatically discovers the feasible solution for the associated attacks.
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Finner, Helmut, Thorsten Dickhaus, and Markus Roters. "Dependency and false discovery rate: Asymptotics." Annals of Statistics 35, no. 4 (2007): 1432–55. http://dx.doi.org/10.1214/009053607000000046.

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Saxena, Hemant, Lukasz Golab, and Ihab F. Ilyas. "Distributed implementations of dependency discovery algorithms." Proceedings of the VLDB Endowment 12, no. 11 (2019): 1624–36. http://dx.doi.org/10.14778/3342263.3342638.

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Ge, Chang, Ihab F. Ilyas, and Florian Kerschbaum. "Secure multi-party functional dependency discovery." Proceedings of the VLDB Endowment 13, no. 2 (2019): 184–96. http://dx.doi.org/10.14778/3364324.3364332.

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Caruccio, Loredana, and Loredana Caruccio. "Monitoring Evolution of Dependency Discovery Results." Journal of Visual Language and Computing 2020, no. 2 (2020): 7–16. http://dx.doi.org/10.18293/jvlc2020-n2-007.

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Papenbrock, Thorsten, Sebastian Kruse, Jorge-Arnulfo Quiané-Ruiz, and Felix Naumann. "Divide & conquer-based inclusion dependency discovery." Proceedings of the VLDB Endowment 8, no. 7 (2015): 774–85. http://dx.doi.org/10.14778/2752939.2752946.

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Aldahami, Abdulelah, Yuefeng Li, and Taizan Chan. "Discovery of dependency relations in sequential data flow." Web Intelligence 15, no. 1 (2017): 35–53. http://dx.doi.org/10.3233/web-170351.

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Yin, Jianwei, Xinkui Zhao, Yan Tang, Chen Zhi, Zuoning Chen, and Zhaohui Wu. "CloudScout: A Non-Intrusive Approach to Service Dependency Discovery." IEEE Transactions on Parallel and Distributed Systems 28, no. 5 (2017): 1271–84. http://dx.doi.org/10.1109/tpds.2016.2619715.

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Dissertations / Theses on the topic "Dependency Discovery"

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Bauckmann, Jana. "Dependency discovery for data integration." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6664/.

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Data integration aims to combine data of different sources and to provide users with a unified view on these data. This task is as challenging as valuable. In this thesis we propose algorithms for dependency discovery to provide necessary information for data integration. We focus on inclusion dependencies (INDs) in general and a special form named conditional inclusion dependencies (CINDs): (i) INDs enable the discovery of structure in a given schema. (ii) INDs and CINDs support the discovery of cross-references or links between schemas. An IND “A in B” simply states that all values of attribute A are included in the set of values of attribute B. We propose an algorithm that discovers all inclusion dependencies in a relational data source. The challenge of this task is the complexity of testing all attribute pairs and further of comparing all of each attribute pair's values. The complexity of existing approaches depends on the number of attribute pairs, while ours depends only on the number of attributes. Thus, our algorithm enables to profile entirely unknown data sources with large schemas by discovering all INDs. Further, we provide an approach to extract foreign keys from the identified INDs. We extend our IND discovery algorithm to also find three special types of INDs: (i) Composite INDs, such as “AB in CD”, (ii) approximate INDs that allow a certain amount of values of A to be not included in B, and (iii) prefix and suffix INDs that represent special cross-references between schemas. Conditional inclusion dependencies are inclusion dependencies with a limited scope defined by conditions over several attributes. Only the matching part of the instance must adhere the dependency. We generalize the definition of CINDs distinguishing covering and completeness conditions and define quality measures for conditions. We propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. The challenge for this task is twofold: (i) Which (and how many) attributes should be used for the conditions? (ii) Which attribute values should be chosen for the conditions? Previous approaches rely on pre-selected condition attributes or can only discover conditions applying to quality thresholds of 100%. Our approaches were motivated by two application domains: data integration in the life sciences and link discovery for linked open data. We show the efficiency and the benefits of our approaches for use cases in these domains.<br>Datenintegration hat das Ziel, Daten aus unterschiedlichen Quellen zu kombinieren und Nutzern eine einheitliche Sicht auf diese Daten zur Verfügung zu stellen. Diese Aufgabe ist gleichermaßen anspruchsvoll wie wertvoll. In dieser Dissertation werden Algorithmen zum Erkennen von Datenabhängigkeiten vorgestellt, die notwendige Informationen zur Datenintegration liefern. Der Schwerpunkt dieser Arbeit liegt auf Inklusionsabhängigkeiten (inclusion dependency, IND) im Allgemeinen und auf der speziellen Form der Bedingten Inklusionsabhängigkeiten (conditional inclusion dependency, CIND): (i) INDs ermöglichen das Finden von Strukturen in einem gegebenen Schema. (ii) INDs und CINDs unterstützen das Finden von Referenzen zwischen Datenquellen. Eine IND „A in B“ besagt, dass alle Werte des Attributs A in der Menge der Werte des Attributs B enthalten sind. Diese Arbeit liefert einen Algorithmus, der alle INDs in einer relationalen Datenquelle erkennt. Die Herausforderung dieser Aufgabe liegt in der Komplexität alle Attributpaare zu testen und dabei alle Werte dieser Attributpaare zu vergleichen. Die Komplexität bestehender Ansätze ist abhängig von der Anzahl der Attributpaare während der hier vorgestellte Ansatz lediglich von der Anzahl der Attribute abhängt. Damit ermöglicht der vorgestellte Algorithmus unbekannte Datenquellen mit großen Schemata zu untersuchen. Darüber hinaus wird der Algorithmus erweitert, um drei spezielle Formen von INDs zu finden, und ein Ansatz vorgestellt, der Fremdschlüssel aus den erkannten INDs filtert. Bedingte Inklusionsabhängigkeiten (CINDs) sind Inklusionsabhängigkeiten deren Geltungsbereich durch Bedingungen über bestimmten Attributen beschränkt ist. Nur der zutreffende Teil der Instanz muss der Inklusionsabhängigkeit genügen. Die Definition für CINDs wird in der vorliegenden Arbeit generalisiert durch die Unterscheidung von überdeckenden und vollständigen Bedingungen. Ferner werden Qualitätsmaße für Bedingungen definiert. Es werden effiziente Algorithmen vorgestellt, die überdeckende und vollständige Bedingungen mit gegebenen Qualitätsmaßen auffinden. Dabei erfolgt die Auswahl der verwendeten Attribute und Attributkombinationen sowie der Attributwerte automatisch. Bestehende Ansätze beruhen auf einer Vorauswahl von Attributen für die Bedingungen oder erkennen nur Bedingungen mit Schwellwerten von 100% für die Qualitätsmaße. Die Ansätze der vorliegenden Arbeit wurden durch zwei Anwendungsbereiche motiviert: Datenintegration in den Life Sciences und das Erkennen von Links in Linked Open Data. Die Effizienz und der Nutzen der vorgestellten Ansätze werden anhand von Anwendungsfällen in diesen Bereichen aufgezeigt.
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Giannella, Chris. "Approximate functional dependency discovery and one-dimensional selectivity estimation." [Bloomington, Ind.] : Indiana University, 2004. http://wwwlib.umi.com/dissertations/fullcit/3162234.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2004.<br>Title from PDF t.p. (viewed Dec. 1, 2008). Source: Dissertation Abstracts International, Volume: 66-01, Section: B, page: 0361. Chair: Edward Robertson.
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Bauckmann, Jana [Verfasser], and Felix [Akademischer Betreuer] Naumann. "Dependency discovery for data integration / Jana Bauckmann. Betreuer: Felix Naumann." Potsdam : Universitätsbibliothek der Universität Potsdam, 2013. http://d-nb.info/1037251431/34.

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Moss, Mark Bomi. "Assessing operational impact in enterprise systems with dependency discovery and usage mining." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31795.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010.<br>Committee Chair: Pu, Calton; Committee Member: Ahamad, Mustaque; Committee Member: Liu, Ling; Committee Member: Mark, Leo; Committee Member: Owen, Henry. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Clawson, John K. "Service Dependency Analysis via TCP/UDP Port Tracing." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5479.

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Enterprise networks are traditionally mapped via layers two or three, providing a view of what devices are connected to different parts of the network infrastructure. A method was developed to map connections at layer four, providing a view of interconnected systems and services instead of network infrastructure. This data was graphed and displayed in a web application. The information proved beneficial in identifying connections between systems or imbalanced clusters when troubleshooting problems with enterprise applications.
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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.<br>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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Fouché, Edouard [Verfasser], and K. [Akademischer Betreuer] Böhm. "Estimating Dependency, Monitoring and Knowledge Discovery in High-Dimensional Data Streams / Edouard Fouché ; Betreuer: K. Böhm." Karlsruhe : KIT-Bibliothek, 2020. http://d-nb.info/1223027821/34.

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Toman, František. "Vylepšení nástroje ServiceNow Discovery v oblastech aplikačních vztahů a konfigurací aplikací." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-193040.

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The main focus of this paper is to improve the ServiceNow Discovery tool in two areas. The first one is the Application Dependency Mapping (ADM). ServiceNow is currently not able to determine the type of the relationship between two applications. As a solution, a new application was designed and implemented. It enables human element to define the type of relationship before it is found by the Discovery. This application also makes it posiible to define rules, which affect the behaviour of ADM. Second area, taken into account in this paper, is the discovery of application configuration. Weaknesses of Discovery are identified and solution is implemented.
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Utku, Selma. "Web Service Testing For Domain Specific Web Service Discovery Framework." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614083/index.pdf.

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The reliability of web services is important for both users and other service providers, with which they are in interaction. Thus, to guarantee reliability of the web services that are invoked and integrated at runtime, automatic testing of web services is needed. In web service testing, different test cases for web services are generated. The most important issue is to generate the most appropriate value for input parameters of web services at runtime. In this thesis, we developed a method for automatic web service testing that uses semantics dependency-based and data mutation-based techniques to analyze web services and generate different test cases. Thus, we both check whether the services function correctly by generating appropriate input values from different data sources and check robustness of web services by generating random and error-prone data inputs. With respect to the behaviors of web services, the test values are calculated and saved to the database for each web service.
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Bauckmann, Jana, Ziawasch Abedjan, Ulf Leser, Heiko Müller, and Felix Naumann. "Covering or complete? : Discovering conditional inclusion dependencies." Universität Potsdam, 2012. http://opus.kobv.de/ubp/volltexte/2012/6208/.

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Data dependencies, or integrity constraints, are used to improve the quality of a database schema, to optimize queries, and to ensure consistency in a database. In the last years conditional dependencies have been introduced to analyze and improve data quality. In short, a conditional dependency is a dependency with a limited scope defined by conditions over one or more attributes. Only the matching part of the instance must adhere to the dependency. In this paper we focus on conditional inclusion dependencies (CINDs). We generalize the definition of CINDs, distinguishing covering and completeness conditions. We present a new use case for such CINDs showing their value for solving complex data quality tasks. Further, we define quality measures for conditions inspired by precision and recall. We propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. Our algorithms choose not only the condition values but also the condition attributes automatically. Finally, we show that our approach efficiently provides meaningful and helpful results for our use case.<br>Datenabhängigkeiten (wie zum Beispiel Integritätsbedingungen), werden verwendet, um die Qualität eines Datenbankschemas zu erhöhen, um Anfragen zu optimieren und um Konsistenz in einer Datenbank sicherzustellen. In den letzten Jahren wurden bedingte Abhängigkeiten (conditional dependencies) vorgestellt, die die Qualität von Daten analysieren und verbessern sollen. Eine bedingte Abhängigkeit ist eine Abhängigkeit mit begrenztem Gültigkeitsbereich, der über Bedingungen auf einem oder mehreren Attributen definiert wird. In diesem Bericht betrachten wir bedingte Inklusionsabhängigkeiten (conditional inclusion dependencies; CINDs). Wir generalisieren die Definition von CINDs anhand der Unterscheidung von überdeckenden (covering) und vollständigen (completeness) Bedingungen. Wir stellen einen Anwendungsfall für solche CINDs vor, der den Nutzen von CINDs bei der Lösung komplexer Datenqualitätsprobleme aufzeigt. Darüber hinaus definieren wir Qualitätsmaße für Bedingungen basierend auf Sensitivität und Genauigkeit. Wir stellen effiziente Algorithmen vor, die überdeckende und vollständige Bedingungen innerhalb vorgegebener Schwellwerte finden. Unsere Algorithmen wählen nicht nur die Werte der Bedingungen, sondern finden auch die Bedingungsattribute automatisch. Abschließend zeigen wir, dass unser Ansatz effizient sinnvolle und hilfreiche Ergebnisse für den vorgestellten Anwendungsfall liefert.
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Books on the topic "Dependency Discovery"

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International Business Machines Corporation. International Technical Support Organization, ed. IBM Tivoli application dependency discovery manager V7.1 deployment guide. IBM, International Technical Support Organization, 2008.

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Healing the child within: Discovery and recovery for adult children of dysfunctional families. Health Communications, 1987.

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Whitfield, Charles L. Healing the child within: Discovery and recovery for adult children of dysfunctional families. Health Communications, 1987.

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Rodale Press. Thr!ve365 scoring guide for eating out: Discover delicious living for diabetes. Rodale, 2010.

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Imagine living without type 2 diabetes: Discover a natural alternative to pharmaceuticals. Advantage Media Group, 2014.

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Peel, William Carr. Discover your destiny: Finding the courage to follow your dreams. NavPress, 1996.

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Konicov, Barrie. Co-Dependency to Self Discovery: Subliminal Persuasion (Master Series). Potentials Unlimited Products, 1990.

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Bart, Jacob, and International Business Machines Corporation. International Technical Support Organization., eds. IBM Tivoli application dependency discovery manager: Capabilities and best practices. IBM, International Technical Support Organization, 2008.

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S, Rapaka Rao, Sorer Heinz, and National Institute on Drug Abuse., eds. Discovery of novel opioid medications. U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Institute on Drug Abuse, 1995.

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(Firm), Rodale, ed. Thr!ve365 scoring guide for eating out: Discover delicious living for diabetes. Rodale, 2010.

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Book chapters on the topic "Dependency Discovery"

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Shaabani, Nuhad, and Christoph Meinel. "Scalable Inclusion Dependency Discovery." In Database Systems for Advanced Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18120-2_25.

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Barone, Daniele, Fabio Stella, and Carlo Batini. "Dependency Discovery in Data Quality." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-642-13094-6_6.

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Lee, Yue-Shi, and Show-Jane Yen. "Classification Based on Attribute Dependency." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30076-2_26.

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Koeller, Andreas, and Elke A. Rundensteiner. "Heuristic Strategies for Inclusion Dependency Discovery." In On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30469-2_5.

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Sharp, Swee, Jenny Howes, and Paul Workman. "CHAPTER 4. Exploiting the Dependency of Cancer Cells on Molecular Chaperones." In Drug Discovery. Royal Society of Chemistry, 2013. http://dx.doi.org/10.1039/9781849739689-00126.

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Akaishi, Mina, Ken Satoh, and Yuzuru Tanaka. "An Associative Information Retrieval Based on the Dependency of Term Co-occurrence." In Discovery Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30214-8_15.

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Birillo, Anastasia, and Nikita Bobrov. "Smart Caching for Efficient Functional Dependency Discovery." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30278-8_7.

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France, Charles P. "Drug-Addiction and Drug-Dependency." In Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-25240-2_10.

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Tenev, V., and M. Nikolova. "Pharmacodynamic Evaluation: Drug Dependency and Addiction." In Drug Discovery and Evaluation: Methods in Clinical Pharmacology. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-68864-0_49.

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Tenev, V., and M. Nikolova. "Pharmacodynamic Evaluation: Drug Dependency and Addiction." In Drug Discovery and Evaluation: Methods in Clinical Pharmacology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-56637-5_49-1.

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Conference papers on the topic "Dependency Discovery"

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Dürsch, Falco, Axel Stebner, Fabian Windheuser, et al. "Inclusion Dependency Discovery." In CIKM '19: The 28th ACM International Conference on Information and Knowledge Management. ACM, 2019. http://dx.doi.org/10.1145/3357384.3357916.

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Jin, Yifeng, Zijing Tan, Weijun Zeng, and Shuai Ma. "Approximate Order Dependency Discovery." In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00010.

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Jin, Yifeng, Lin Zhu, and Zijing Tan. "Efficient Bidirectional Order Dependency Discovery." In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00013.

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Ding, Min, Vishal Singh, Yueping Zhang, and Guofei Jiang. "Application dependency discovery using matrix factorization." In 2012 IEEE 20th International Workshop on Quality of Service (IWQoS). IEEE, 2012. http://dx.doi.org/10.1109/iwqos.2012.6245965.

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Papenbrock, Thorsten, and Felix Naumann. "A Hybrid Approach to Functional Dependency Discovery." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2882903.2915203.

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Weichselbraun, Albert, and Norman Süsstrunk. "Optimizing Dependency Parsing Throughput." In 7th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005638905110516.

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"GIVING SHAPE TO AN N–VERSION DEPENDENCY PARSER - Improving Dependency Parsing Accuracy for Spanish using Maltparser." In International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003116803360341.

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Basu, Sujoy, Fabio Casati, and Florian Daniel. "Web Service Dependency Discovery Tool for SOA Management." In 2007 IEEE International Conference on Services Computing. IEEE, 2007. http://dx.doi.org/10.1109/scc.2007.130.

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Basu, Sujoy, Fabio Casati, and Florian Daniel. "Toward Web Service Dependency Discovery for SOA Management." In 2008 IEEE International Conference on Services Computing (SCC). IEEE, 2008. http://dx.doi.org/10.1109/scc.2008.45.

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Han, Ho-Young, Yeon-Seok Kim, and Kyong-Ho Lee. "Semantic service discovery based on parametric dependency relations." In 2011 International Conference on Information Networking (ICOIN). IEEE, 2011. http://dx.doi.org/10.1109/icoin.2011.5723154.

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Reports on the topic "Dependency Discovery"

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Teer, Jamie, and Anindya Dutta. Discovery and Development of Inhibitors that Selectively Interfere with Cyclin-Dependent Kinase Substrate Recognition. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada430337.

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Tear, Jamie, and Anindya Dutta. Discovery and Development of Inhibitors That Selectively Interfere With Cyclin-Dependent Kinase Substrate Recognition. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada420174.

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