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Relevant bibliographies by topics / Semantics - Data processing

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Academic literature on the topic 'Semantics - Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Semantics - Data processing"

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Thirunarayan, Krishnaprasad, and Amit Sheth. "Semantics-Empowered Big Data Processing with Applications." AI Magazine 36, no. 1 (March 25, 2015): 39–54. http://dx.doi.org/10.1609/aimag.v36i1.2566.

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We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical-cyber-social systems. To handle volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle veracity, we explore trust models and approaches to glean trustworthiness. These four v's of big data are harnessed by the semantics-empowered analytics to derive value to support applications transcending physical-cyber-social continuum.

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Jin, Min. "Semantics in XML Data Processing." Journal of the Korea Academia-Industrial cooperation Society 12, no. 3 (March 31, 2011): 1327–35. http://dx.doi.org/10.5762/kais.2011.12.3.1327.

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Liu, Jun Qiang, and Xiao Ling Guan. "Composite Event Processing for Data Streams and Domain Knowledge." Advanced Materials Research 219-220 (March 2011): 927–31. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.927.

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In recent years the processing of composite event queries over data streams has attracted a lot of research attention. Traditional database techniques were not designed for stream processing system. Furthermore, example continuous queries are often formulated in declarative query language without specifying the semantics. To overcome these deficiencies, this article presents the design, implementation, and evaluation of a system that executes data streams with semantic information. Then, a set of optimization techniques are proposed for handling query. So, our approach not only makes it possible to express queries with a sound semantics, but also provides a solid foundation for query optimization. Experiment results show that our approach is effective and efficient for data streams and domain knowledge.

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Xhafa, Fatos, and Leonard Barolli. "Semantics, intelligent processing and services for big data." Future Generation Computer Systems 37 (July 2014): 201–2. http://dx.doi.org/10.1016/j.future.2014.02.004.

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Ramprasad, R., and C. Jayakumari. "A Novel Approach for Mining Big Data Using Multi-Model Fusion Mechanism (MMFM)." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (June 9, 2023): 484–93. http://dx.doi.org/10.17762/ijritcc.v11i5s.7110.

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Big data processing and analytics require sophisticated systems and cutting-edge methodologies to extract useful data from the available data. Extracted data visualization is challenging because of the processing models' dependence on semantics and classification. To categorize and improve information-based semantics that have accumulated over time, this paper introduces the Multi-model fusion mechanism for data mining (MMFM) approach. Information dependencies are organized based on the links between the data model based on attribute values. This method divides the attributes under consideration based on processing time to handle complicated data in controlled amount of time. The proposed MMFM’s performance is assessed with real-time weather prediction dataset where the data is acquired from sensor (observed) and image data. MMFM is used to conduct semantic analytics and similarity-based classification on this collection. The processing time based on records and samples are investigated for the various data sizes, instances, and entries. It is found that the proposed MMFM gets 70 seconds of processing time for 2GB data and 0.99 seconds while handling 5000 records for various classification instances.

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NISHIMURA, SUSUMU, and ATSUSHI OHORI. "Parallel functional programming on recursively defined data via data-parallel recursion." Journal of Functional Programming 9, no. 4 (July 1999): 427–62. http://dx.doi.org/10.1017/s0956796899003457.

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This article proposes a new language mechanism for data-parallel processing of dynamically allocated recursively defined data. Different from the conventional array-based data- parallelism, it allows parallel processing of general recursively defined data such as lists or trees in a functional way. This is achieved by representing a recursively defined datum as a system of equations, and defining new language constructs for parallel transformation of a system of equations. By integrating them with a higher-order functional language, we obtain a functional programming language suitable for describing data-parallel algorithms on recursively defined data in a declarative way. The language has an ML style polymorphic type system and a type sound operational semantics that uniformly integrates the parallel evaluation mechanism with the semantics of a typed functional language. We also show the intended parallel execution model behind the formal semantics, assuming an idealized distributed memory multicomputer.

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Boyle, Mary. "Semantic Treatments for Word and Sentence Production Deficits in Aphasia." Seminars in Speech and Language 38, no. 01 (February 2017): 052–61. http://dx.doi.org/10.1055/s-0036-1597256.

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The cognitive domains of language and memory are intrinsically connected and work together during language processing. This relationship is especially apparent in the area of semantics. Several disciplines have contributed to a rich store of data about semantic organization and processing, and several semantic treatments for aphasic word and sentence production impairments have been based on these data. This article reviews the relationships between semantics and memory as they relate to word and sentence production, describes the aphasic language impairments that result from deficits in these areas, and summarizes treatment approaches that capitalize on what we have learned about these domains and how they work together.

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Sejdiu, Besmir, Florije Ismaili, and Lule Ahmedi. "Integration of Semantics Into Sensor Data for the IoT." International Journal on Semantic Web and Information Systems 16, no. 4 (October 2020): 1–25. http://dx.doi.org/10.4018/ijswis.2020100101.

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The internet of things (IoT) as an evolving technology represents an active scientific research field in recognizing research challenges associated with its application in various domains, ranging from consumer convenience, smart energy, and resource saving to IoT enterprises. Sensors are crucial components of IoT that relay the collected data in the form of the data stream for further processing. Interoperability of various connected digital resources is a key challenge in IoT environments. The enrichment of raw sensor data with semantic annotations using concept definitions from ontologies enables more expressive data representation that supports knowledge discovery. In this paper, a systematic review of integration of semantics into sensor data for the IoT is provided. The conducted review is focused on analyzing the main solutions of adding semantic annotations to the sensor data, standards that enable all types of sensor data via the web, existing models of stream data annotation, and the IoT trend domains that use semantics.

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Nguyen Mau Quoc, Hoan, Martin Serrano, Han Mau Nguyen, John G. Breslin, and Danh Le-Phuoc. "EAGLE—A Scalable Query Processing Engine for Linked Sensor Data." Sensors 19, no. 20 (October 9, 2019): 4362. http://dx.doi.org/10.3390/s19204362.

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Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.

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Kim, Hyeon Gyu. "Exploiting Window Query Semantics in Scalable Data Stream Processing." International Journal of Control and Automation 8, no. 11 (November 30, 2015): 13–20. http://dx.doi.org/10.14257/ijca.2015.8.11.02.

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Dissertations / Theses on the topic "Semantics - Data processing"

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鄧偉明 and Wai-ming Tang. "Semantics of authentication in workflow security." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B30110828.

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Sax, Matthias J. "Performance Optimizations and Operator Semantics for Streaming Data Flow Programs." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21424.

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Unternehmen sammeln mehr Daten als je zuvor und müssen auf diese Informationen zeitnah reagieren. Relationale Datenbanken eignen sich nicht für die latenzfreie Verarbeitung dieser oft unstrukturierten Daten. Um diesen Anforderungen zu begegnen, haben sich in der Datenbankforschung seit dem Anfang der 2000er Jahre zwei neue Forschungsrichtungen etabliert: skalierbare Verarbeitung unstrukturierter Daten und latenzfreie Datenstromverarbeitung. Skalierbare Verarbeitung unstrukturierter Daten, auch bekannt unter dem Begriff "Big Data"-Verarbeitung, hat in der Industrie schnell Einzug erhalten. Gleichzeitig wurden in der Forschung Systeme zur latenzfreien Datenstromverarbeitung entwickelt, die auf eine verteilte Architektur, Skalierbarkeit und datenparallele Verarbeitung setzen. Obwohl diese Systeme in der Industrie vermehrt zum Einsatz kommen, gibt es immer noch große Herausforderungen im praktischen Einsatz. Diese Dissertation verfolgt zwei Hauptziele: Zuerst wird das Laufzeitverhalten von hochskalierbaren datenparallelen Datenstromverarbeitungssystemen untersucht. Im zweiten Hauptteil wird das "Dual Streaming Model" eingeführt, das eine Semantik zur gleichzeitigen Verarbeitung von Datenströmen und Tabellen beschreibt. Das Ziel unserer Untersuchung ist ein besseres Verständnis über das Laufzeitverhalten dieser Systeme zu erhalten und dieses Wissen zu nutzen um Anfragen automatisch ausreichende Rechenkapazität zuzuweisen. Dazu werden ein Kostenmodell und darauf aufbauende Optimierungsalgorithmen für Datenstromanfragen eingeführt, die Datengruppierung und Datenparallelität einbeziehen. Das vorgestellte Datenstromverarbeitungsmodell beschreibt das Ergebnis eines Operators als kontinuierlichen Strom von Veränderugen auf einer Ergebnistabelle. Dabei behandelt unser Modell die Diskrepanz der physikalischen und logischen Ordnung von Datenelementen inhärent und erreicht damit eine deterministische Semantik und eine minimale Verarbeitungslatenz.
Modern companies are able to collect more data and require insights from it faster than ever before. Relational databases do not meet the requirements for processing the often unstructured data sets with reasonable performance. The database research community started to address these trends in the early 2000s. Two new research directions have attracted major interest since: large-scale non-relational data processing as well as low-latency data stream processing. Large-scale non-relational data processing, commonly known as "Big Data" processing, was quickly adopted in the industry. In parallel, low latency data stream processing was mainly driven by the research community developing new systems that embrace a distributed architecture, scalability, and exploits data parallelism. While these systems have gained more and more attention in the industry, there are still major challenges to operate them at large scale. The goal of this dissertation is two-fold: First, to investigate runtime characteristics of large scale data-parallel distributed streaming systems. And second, to propose the "Dual Streaming Model" to express semantics of continuous queries over data streams and tables. Our goal is to improve the understanding of system and query runtime behavior with the aim to provision queries automatically. We introduce a cost model for streaming data flow programs taking into account the two techniques of record batching and data parallelization. Additionally, we introduce optimization algorithms that leverage our model for cost-based query provisioning. The proposed Dual Streaming Model expresses the result of a streaming operator as a stream of successive updates to a result table, inducing a duality between streams and tables. Our model handles the inconsistency of the logical and the physical order of records within a data stream natively, which allows for deterministic semantics as well as low latency query execution.

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Nedas, Konstantinos A. "Semantic Similarity of Spatial Scenes." Fogler Library, University of Maine, 2006. http://www.library.umaine.edu/theses/pdf/NedasKA2006.pdf.

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Giese, Holger, Stephan Hildebrandt, and Leen Lambers. "Toward bridging the gap between formal semantics and implementation of triple graph grammars." Universität Potsdam, 2010. http://opus.kobv.de/ubp/volltexte/2010/4521/.

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The correctness of model transformations is a crucial element for the model-driven engineering of high quality software. A prerequisite to verify model transformations at the level of the model transformation specification is that an unambiguous formal semantics exists and that the employed implementation of the model transformation language adheres to this semantics. However, for existing relational model transformation approaches it is usually not really clear under which constraints particular implementations are really conform to the formal semantics. In this paper, we will bridge this gap for the formal semantics of triple graph grammars (TGG) and an existing efficient implementation. Whereas the formal semantics assumes backtracking and ignores non-determinism, practical implementations do not support backtracking, require rule sets that ensure determinism, and include further optimizations. Therefore, we capture how the considered TGG implementation realizes the transformation by means of operational rules, define required criteria and show conformance to the formal semantics if these criteria are fulfilled. We further outline how static analysis can be employed to guarantee these criteria.

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Wong, Ping-wai, and 黃炳蔚. "Semantic annotation of Chinese texts with message structures based on HowNet." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38212389.

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Herre, Heinrich, and Axel Hummel. "A paraconsistent semantics for generalized logic programs." Universität Potsdam, 2010. http://opus.kobv.de/ubp/volltexte/2010/4149/.

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We propose a paraconsistent declarative semantics of possibly inconsistent generalized logic programs which allows for arbitrary formulas in the body and in the head of a rule (i.e. does not depend on the presence of any specific connective, such as negation(-as-failure), nor on any specific syntax of rules). For consistent generalized logic programs this semantics coincides with the stable generated models introduced in [HW97], and for normal logic programs it yields the stable models in the sense of [GL88].

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Lamprecht, Anna-Lena, Tiziana Margaria, and Bernhard Steffen. "Bio-jETI : a framework for semantics-based service composition." Universität Potsdam, 2009. http://opus.kobv.de/ubp/volltexte/2010/4506/.

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Background: The development of bioinformatics databases, algorithms, and tools throughout the last years has lead to a highly distributedworld of bioinformatics services. Without adequatemanagement and development support, in silico researchers are hardly able to exploit the potential of building complex, specialized analysis processes from these services. The Semantic Web aims at thoroughly equipping individual data and services with machine-processable meta-information, while workflow systems support the construction of service compositions. However, even in this combination, in silico researchers currently would have to deal manually with the service interfaces, the adequacy of the semantic annotations, type incompatibilities, and the consistency of service compositions. Results: In this paper, we demonstrate by means of two examples how Semantic Web technology together with an adequate domain modelling frees in silico researchers from dealing with interfaces, types, and inconsistencies. In Bio-jETI, bioinformatics services can be graphically combined to complex services without worrying about details of their interfaces or about type mismatches of the composition. These issues are taken care of at the semantic level by Bio-jETI’s model checking and synthesis features. Whenever possible, they automatically resolve type mismatches in the considered service setting. Otherwise, they graphically indicate impossible/incorrect service combinations. In the latter case, the workflow developermay either modify his service composition using semantically similar services, or ask for help in developing the missing mediator that correctly bridges the detected type gap. Newly developed mediators should then be adequately annotated semantically, and added to the service library for later reuse in similar situations. Conclusion: We show the power of semantic annotations in an adequately modelled and semantically enabled domain setting. Using model checking and synthesis methods, users may orchestrate complex processes from a wealth of heterogeneous services without worrying about interfaces and (type) consistency. The success of this method strongly depends on a careful semantic annotation of the provided services and on its consequent exploitation for analysis, validation, and synthesis. We are convinced that these annotations will become standard, as they will become preconditions for the success and widespread use of (preferred) services in the Semantic Web

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Zhan, Tianjie. "Semantic analysis for extracting fine-grained opinion aspects." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1213.

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Otten, Frederick John. "Using semantic knowledge to improve compression on log files." Thesis, Rhodes University, 2008. http://eprints.ru.ac.za/1660/.

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Harrison, Dave. "Functional real-time programming : the language Ruth and its semantics." Thesis, University of Stirling, 1988. http://hdl.handle.net/1893/12116.

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Real-time systems are amongst the most safety critical systems involving computer software and the incorrect functioning of this software can cause great damage, up to and including the loss of life. If seems sensible therefore to write real-time software in a way that gives us the best chance of correctly implementing specifications. Because of the high level of functional programming languages, their semantic simplicity and their amenability to formal reasoning and correctness preserving transformation it thus seems natural to use a functional language for this task. This thesis explores the problems of applying functional programming languages to real-time by defining the real-time functional programming language Ruth. The first part of the thesis concerns the identification of the particular problems associated with programming real-time systems. These can broadly be stated as a requirement that a real-time language must be able to express facts about time, a feature we have called time expressibility. The next stage is to provide time expressibility within a purely functional framework. This is accomplished by the use of timestamps on inputs and outputs and by providing a real-time clock as an input to Ruth programs. The final major part of the work is the construction of a formal definition of the semantics of Ruth to serve as a basis for formal reasoning and transformation. The framework within which the formal semantics of a real-time language are defined requires time expressibility in the same way as the real-time language itself. This is accomplished within the framework of domain theory by the use of specialised domains for timestamped objects, called herring-bone domains. These domains could be used as the basis for the definition of the semantics of any real-time language.

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Books on the topic "Semantics - Data processing"

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Palmer, Martha Stone. Semantic processing for finite domains. Cambridge [England]: Cambridge University Press, 1990.

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1954-, Saint-Dizier Patrick, and Viegas Evelyne, eds. Computational lexical semantics. Cambridge [England]: Cambridge University Press, 1995.

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Egg, Markus. Flexible semantics for reinterpretation phenomena. Stanford, Calif: Center for the Study of Language and Information, 2005.

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Egg, Markus. Flexible semantics for reinterpretation phenomena. Stanford, Calif: CSLI Publications, 2006.

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Fox, Chris. Foundations of intensional semantics. Malden MA: Blackwell Pub., 2005.

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Fass, Dan. Processing metonymy and metaphor. Greenwich, Conn: Ablex Pub. Corp., 1997.

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David, Hutchison. Journal on Data Semantics XII. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Spaccapietra, S. Journal on Data Semantics XV. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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C, Bunt Harry, Muskens Reinhard 1953-, and Thijsse Elias, eds. Computing meaning. Dordrecht ; Boston: Kluwer Academic, 1999.

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Nico, Weber, ed. Semantik, Lexikographie und Computeranwendungen. Tübingen: Niemeyer, 1996.

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Book chapters on the topic "Semantics - Data processing"

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Torre-Bastida, Ana I. "Incremental SPARQL Query Processing." In The Semantic Web: Semantics and Big Data, 712–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38288-8_57.

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Scharrenbach, Thomas, Jacopo Urbani, Alessandro Margara, Emanuele Della Valle, and Abraham Bernstein. "Seven Commandments for Benchmarking Semantic Flow Processing Systems." In The Semantic Web: Semantics and Big Data, 305–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38288-8_21.

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Torres, Miguel, Rolando Quintero, Marco Moreno, and Frederico Fonseca. "Ontology-Driven Description of Spatial Data for Their Semantic Processing." In GeoSpatial Semantics, 242–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11586180_18.

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Sikström, Sverker, Oscar N. E. Kjell, and Katarina Kjell. "SemanticExcel.com: An Online Software for Statistical Analyses of Text Data Based on Natural Language Processing." In Statistical Semantics, 87–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37250-7_6.

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Lichtenstein, Tom, and Mathias Weske. "Execution Semantics for Process Choreographies with Data." In Lecture Notes in Business Information Processing, 90–106. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-41623-1_6.

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Albrecht, Meike, Edith Buchholz, Antje Düsterhöft, and Bernhard Thalheim. "An informal and efficient approach for obtaining semantic constraints using sample data and natural language processing." In Semantics in Databases, 1–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0035003.

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Oltramari, Alessandro. "An Introduction to Hybrid Semantics: The Role of Cognition in Semantic Resources." In Modeling, Learning, and Processing of Text Technological Data Structures, 97–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22613-7_6.

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Levaire, Jean-Luc. "Semantics and compilation of the data-parallel switch statement." In Parallel Processing: CONPAR 92—VAPP V, 491–502. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55895-0_447.

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Gerner, Philippe, and Eric Violard. "A Theoretical Framework of Data Parallelism and Its Operational Semantics." In Euro-Par 2000 Parallel Processing, 668–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44520-x_92.

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Grüger, Joscha, Tobias Geyer, David Jilg, and Ralph Bergmann. "SAMPLE: A Semantic Approach for Multi-perspective Event Log Generation." In Lecture Notes in Business Information Processing, 328–40. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_24.

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AbstractData and process mining techniques can be applied in many areas to gain valuable insights. For many reasons, accessibility to real-world business and medical data is severely limited. However, research, but especially the development of new methods, depends on a sufficient basis of realistic data. Due to the lack of data, this progress is hindered. This applies in particular to domains that use personal data, such as healthcare. With adequate quality, synthetic data can be a solution to this problem. In the procedural field, some approaches have already been presented that generate synthetic data based on a process model. However, only a few have included the data perspective so far. Data semantics, which is crucial for the quality of the generated data, has not yet been considered. Therefore, in this paper we present the multi-perspective event log generation approach SAMPLE that considers the data perspective and, in particular, its semantics. The evaluation of the approach is based on a process model for the treatment of malignant melanoma. As a result, we were able to integrate the semantic of data into the log generation process and identify new challenges.

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Conference papers on the topic "Semantics - Data processing"

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Quoc, Hoan Nguyen Mau, and Danh Le Phuoc. "An elastic and scalable spatiotemporal query processing for linked sensor data." In SEMANTiCS '15: 11th International Conference on Semantic Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2814864.2814869.

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Keppmann, Felix Leif, Maria Maleshkova, and Andreas Harth. "Adaptable Interfaces, Interactions, and Processing for Linked Data Platform Components." In Semantics2017: Semantics 2017 - 13th International Conference on Semantic Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3132218.3132243.

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Kuka, Christian, and Daniela Nicklas. "Enriching sensor data processing with quality semantics." In 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS). IEEE, 2014. http://dx.doi.org/10.1109/percomw.2014.6815246.

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Arora, Vaibhav, Faisal Nawab, Divyakant Agrawal, and Amr El Abbadi. "Typhon: Consistency Semantics for Multi-Representation Data Processing." In 2017 IEEE 10th International Conference on Cloud Computing (CLOUD). IEEE, 2017. http://dx.doi.org/10.1109/cloud.2017.87.

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Ge, Tingjian. "Join queries on uncertain data: Semantics and efficient processing." In 2011 IEEE International Conference on Data Engineering (ICDE 2011). IEEE, 2011. http://dx.doi.org/10.1109/icde.2011.5767888.

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Ku, Tao, YunLong Zhu, and KunYuan Hu. "Semantics-Based Complex Event Processing for RFID Data Streams." In The First International Symposium on Data, Privacy, and E-Commerce (ISDPE 2007). IEEE, 2007. http://dx.doi.org/10.1109/isdpe.2007.9.

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Kim, HyeongSik, Padmashree Ravindra, and Kemafor Anyanwu. "A semantics-aware storage framework for scalable processing of knowledge graphs on Hadoop." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8257927.

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Bodziony, Michał, Hubert Krzyżanowski, Łukasz Pięta, and Robert Wrembel. "On discovering semantics of user-defined functions in data processing workflows." In SIGMOD/PODS '21: International Conference on Management of Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460866.3461771.

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Ren, Yongli, Gang Li, and Wanlei Zhou. "Recommendation Techniques Based on Off-Line Data Processing a Multifaceted Survey." In 2013 Ninth International Conference on Semantics, Knowledge and Grids (SKG). IEEE, 2013. http://dx.doi.org/10.1109/skg.2013.23.

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Karim, Farah, Mohamed Nadjib Mami, Maria-Esther Vidal, and Sören Auer. "Large-scale storage and query processing for semantic sensor data." In WIMS '17: 7th International Conference on Web Intelligence, Mining and Semantics. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3102254.3102260.

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Reports on the topic "Semantics - Data processing"

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Zelenskyi, Arkadii A. Relevance of research of programs for semantic analysis of texts and review of methods of their realization. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2884.

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One of the main tasks of applied linguistics is the solution of the problem of high-quality automated processing of natural language. The most popular methods for processing natural-language text responses for the purpose of extraction and representation of semantics should be systems that are based on the efficient combination of linguistic analysis technologies and analysis methods. Among the existing methods for analyzing text data, a valid method is used by the method using a vector model. Another effective and relevant means of extracting semantics from the text and its representation is the method of latent semantic analysis (LSA). The LSA method was tested and confirmed its effectiveness in such areas of processing the native language as modeling the conceptual knowledge of the person; information search, the implementation of which LSA shows much better results than conventional vector methods.

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Borgwardt, Stefan, Walter Forkel, and Alisa Kovtunova. Finding New Diamonds: Temporal Minimal-World Query Answering over Sparse ABoxes. Technische Universität Dresden, 2019. http://dx.doi.org/10.25368/2023.223.

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Lightweight temporal ontology languages have become a very active field of research in recent years. Many real-world applications, like processing electronic health records (EHRs), inherently contain a temporal dimension, and require efficient reasoning algorithms. Moreover, since medical data is not recorded on a regular basis, reasoners must deal with sparse data with potentially large temporal gaps. In this paper, we introduce a temporal extension of the tractable language ELH⊥, which features a new class of convex diamond operators that can be used to bridge temporal gaps. We develop a completion algorithm for our logic, which shows that entailment remains tractable. Based on this, we develop a minimal-world semantics for answering metric temporal conjunctive queries with negation. We show that query answering is combined first-order rewritable, and hence in polynomial time in data complexity.

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Patwa, B., P. L. St-Charles, G. Bellefleur, and B. Rousseau. Predictive models for first arrivals on seismic reflection data, Manitoba, New Brunswick, and Ontario. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329758.

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First arrivals are the primary waves picked and analyzed by seismologists to infer properties of the subsurface. Here we try to solve a problem in a small subsection of the seismic processing workflow: first break picking of seismic reflection data. We formulate this problem as an image segmentation task. Data is preprocessed, cleaned from outliers and extrapolated to make the training of deep learning models feasible. We use Fully Convolutional Networks (specifically UNets) to train initial models and explore their performance with losses, layer depths, and the number of classes. We propose to use residual connections to improve each UNet block and residual paths to solve the semantic gap between UNet encoder and decoder which improves the performance of the model. Adding spatial information as an extra channel helped increase the RMSE performance of the first break predictions. Other techniques like data augmentation, multitask loss, and normalization methods, were further explored to evaluate model improvement.

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