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Academic literature on the topic 'Process querying'

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Published: 1 June 2024

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Contents

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

Journal articles on the topic "Process querying":

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Wang, Jianmin, Tao Jin, Raymond K. Wong, and Lijie Wen. "Querying business process model repositories." World Wide Web 17, no. 3 (April 13, 2013): 427–54. http://dx.doi.org/10.1007/s11280-013-0210-z.

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CHANDRA, S., D. I. BLOCKLEY, and N. J. WOODMAN. "QUALITATIVE QUERYING OF PHYSICAL PROCESS SIMULATIONS." Civil Engineering Systems 10, no. 3 (July 1993): 225–42. http://dx.doi.org/10.1080/02630259308970125.

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Kunze, Matthias, Matthias Weidlich, and Mathias Weske. "Querying process models by behavior inclusion." Software & Systems Modeling 14, no. 3 (December 3, 2013): 1105–25. http://dx.doi.org/10.1007/s10270-013-0389-6.

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Ngoc Chan, Nguyen, Nattawat Nonsung, and Walid Gaaloul. "Service querying to support process variant development." Journal of Systems and Software 122 (December 2016): 538–52. http://dx.doi.org/10.1016/j.jss.2015.07.050.

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Jin, Tao, Jianmin Wang, Marcello La Rosa, Arthur ter Hofstede, and Lijie Wen. "Efficient querying of large process model repositories." Computers in Industry 64, no. 1 (January 2013): 41–49. http://dx.doi.org/10.1016/j.compind.2012.09.008.

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Polyvyanyy, Artem, Chun Ouyang, Alistair Barros, and Wil M. P. van der Aalst. "Process querying: Enabling business intelligence through query-based process analytics." Decision Support Systems 100 (August 2017): 41–56. http://dx.doi.org/10.1016/j.dss.2017.04.011.

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Merazi, Afaf, and Mimoun Malki. "SQUIREL: Semantic Querying Interlinked OWL-S traveling Process Models." International Journal of Information Technology and Computer Science 7, no. 12 (November 8, 2015): 30–39. http://dx.doi.org/10.5815/ijitcs.2015.12.04.

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P, Ezhumalai, and Ezhilvanji S. "Bandwidth Optimized Resource Allocation Process in Querying Distributed Storage." International Innovative Research Journal of Engineering and Technology 3, no. 3 (March 31, 2018): 18–21. http://dx.doi.org/10.32595/iirjet.org/v3i3.2018.61.

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Cao, Bin, Jiaxing Wang, Jing Fan, Jianwei Yin, and Tianyang Dong. "Querying Similar Process Models Based on the Hungarian Algorithm." IEEE Transactions on Services Computing 10, no. 1 (January 1, 2017): 121–35. http://dx.doi.org/10.1109/tsc.2016.2597143.

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Polyvyanyy, Artem, Anastasiia Pika, and Arthur H. M. ter Hofstede. "Scenario-based process querying for compliance, reuse, and standardization." Information Systems 93 (November 2020): 101563. http://dx.doi.org/10.1016/j.is.2020.101563.

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Journal articles

Dissertations / Theses on the topic "Process querying":

1

Kunze, Matthias. "Searching business process models by example." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2013/6884/.

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Business processes are fundamental to the operations of a company. Each product manufactured and every service provided is the result of a series of actions that constitute a business process. Business process management is an organizational principle that makes the processes of a company explicit and offers capabilities to implement procedures, control their execution, analyze their performance, and improve them. Therefore, business processes are documented as process models that capture these actions and their execution ordering, and make them accessible to stakeholders. As these models are an essential knowledge asset, they need to be managed effectively. In particular, the discovery and reuse of existing knowledge becomes challenging in the light of companies maintaining hundreds and thousands of process models. In practice, searching process models has been solved only superficially by means of free-text search of process names and their descriptions. Scientific contributions are limited in their scope, as they either present measures for process similarity or elaborate on query languages to search for particular aspects. However, they fall short in addressing efficient search, the presentation of search results, and the support to reuse discovered models. This thesis presents a novel search method, where a query is expressed by an exemplary business process model that describes the behavior of a possible answer. This method builds upon a formal framework that captures and compares the behavior of process models by the execution ordering of actions. The framework contributes a conceptual notion of behavioral distance that quantifies commonalities and differences of a pair of process models, and enables process model search. Based on behavioral distances, a set of measures is proposed that evaluate the quality of a particular search result to guide the user in assessing the returned matches. A projection of behavioral aspects to a process model enables highlighting relevant fragments that led to a match and facilitates its reuse. The thesis further elaborates on two search techniques that provide concrete behavioral distance functions as an instantiation of the formal framework. Querying enables search with a notion of behavioral inclusion with regard to the query. In contrast, similarity search obtains process models that are similar to a query, even if the query is not precisely matched. For both techniques, indexes are presented that enable efficient search. Methods to evaluate the quality and performance of process model search are introduced and applied to the techniques of this thesis. They show good results with regard to human assessment and scalability in a practical setting.
Geschäftsprozesse bilden die Grundlage eines jeden Unternehmens, da jedes Produkt und jede Dienstleistung das Ergebnis einer Reihe von Arbeitsschritten sind, deren Ablauf einen Geschäftsprozess darstellen. Das Geschäftsprozessmanagement rückt diese Prozesse ins Zentrum der Betrachtung und stellt Methoden bereit, um Prozesse umzusetzen, abzuwickeln und, basierend auf einer Auswertung ihrer Ausführung, zu verbessern. Zu diesem Zweck werden Geschäftsprozesse in Form von Prozessmodellen dokumentiert, welche die auszuführenden Arbeitsschritte und ihre Ausführungsbeziehungen erfassen und damit eine wesentliche Grundlage des Geschäftsprozessmanagements bilden. Um dieses Wissen verwerten zu können, muss es gut organisiert und leicht auffindbar sein – eine schwierige Aufgabe angesichts hunderter bzw. tausender Prozessmodelle, welche moderne Unternehmen unterhalten. In der Praxis haben sich bisher lediglich einfache Suchmethoden etabliert, zum Beispiel Freitextsuche in Prozessbeschreibungen. Wissenschaftliche Ansätze hingegen betrachten Ähnlichkeitsmaße und Anfragesprachen für Prozessmodelle, vernachlässigen dabei aber Maßnahmen zur effizienten Suche, sowie die verständliche Wiedergabe eines Suchergebnisses und Hilfestellungen für dessen Verwendung. Diese Dissertation stellt einen neuen Ansatz für die Prozessmodellsuche vor, wobei statt einer Anfragesprache Prozessmodelle zur Formulierung einer Anfrage verwendet werden, welche exemplarisch das Verhalten der gesuchten Prozesse beschreiben. Dieser Ansatz fußt auf einem formalen Framework, welches ein konzeptionelles Distanzmaß zur Bewertung gemeinsamen Verhaltens zweier Geschäftsprozesse definiert und die Grundlage zur Suche bildet. Darauf aufbauend werden Qualitätsmaße vorgestellt, die einem Benutzer bei der Bewertung von Suchergebnissen behilflich sind. Verhaltensausschnitte, die zur Aufnahme in das Suchergebnis geführt haben, können im Prozessmodell hervorgehoben werden. Die Arbeit führt zwei Suchtechniken ein, die konkrete Distanzmaße einsetzen, um Prozesse zu suchen, die das Verhalten einer Anfrage exakt enthalten (Querying), oder diesem in Bezug auf das Verhalten ähnlich sind (Similarity Search). Für beide Techniken werden Indexstrukturen vorgestellt, die effizientes Suchen ermöglichen. Abschließend werden allgemeine Methoden zur Evaluierung von Prozessmodellsuchansätzen vorgestellt, mit welchen die genannten Suchtechniken überprüft werden. Im Ergebnis zeigen diese eine hohe Qualität der Suchergebnisse hinsichtlich einer Vergleichsstudie mit Prozessexperten, sowie gute Skalierbarkeit für große Prozessmodellsammlungen.
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Kobeissi, Meriana. "A conversational AI Framework for Cognitive Process Analysis." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS025.

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Les processus métier (BP) sont les piliers fondamentaux des organisations, englobant toute une gamme d'activités structurées visant à atteindre des objectifs organisationnels distincts. Ces processus, caractérisés par une multitude de tâches, d'interactions et de flux de travail, offrent une méthodologie structurée pour superviser les opérations cruciales dans divers secteurs. Une découverte essentielle pour les organisations a été la reconnaissance de la valeur profonde inhérente aux données produites pendant ces processus. L'analyse des processus, une discipline spécialisée, explore ces journaux de données, facilitant une compréhension plus profonde et l'amélioration des BP. Cette analyse peut être catégorisée en deux perspectives : le niveau d'instance, qui se concentre sur les exécutions individuelles de processus, et le niveau de processus, qui examine le processus global.Cependant, l'application de l'analyse des processus pose des défis aux utilisateurs, impliquant la nécessité d'accéder aux données, de naviguer dans les API de bas niveau et d'utiliser des méthodes dépendantes d'outils. L'application dans le monde réel rencontre souvent des complexités et des obstacles centrés sur l'utilisateur.Plus précisément, l'analyse de niveau d'instance exige des utilisateurs qu'ils accèdent aux données d'exécution de processus stockées, une tâche qui peut être complexe pour les professionnels de l'entreprise en raison de l'exigence de maîtriser des langages de requête complexes tels que SQL et CYPHER. En revanche, l'analyse de niveau de processus des données de processus implique l'utilisation de méthodes et d'algorithmes qui exploitent les données d'exécution de processus extraites des systèmes d'information. Ces méthodologies sont regroupées sous le terme de techniques d'exploration de processus. L'application de l'exploration de processus confronte les analystes à la tâche complexe de sélection de méthodes, qui consiste à trier des descriptions de méthodes non structurées. De plus, l'application des méthodes d'exploration de processus dépend d'outils spécifiques et nécessite un certain niveau d'expertise technique.Pour relever ces défis, cette thèse présente des solutions basées sur l'IA, mettant l'accent sur l'intégration de capacités cognitives dans l'analyse des processus pour faciliter les tâches d'analyse tant au niveau de l'instance qu'au niveau du processus pour tous les utilisateurs. Les objectifs principaux sont doubles : premièrement, améliorer l'accessibilité des données d'exécution de processus en créant une interface capable de construire automatiquement la requête de base correspondante à partir du langage naturel. Ceci est complété par la proposition d'une technique de stockage adaptée et d'un langage de requête autour desquels l'interface doit être conçue. À cet égard, nous introduisons un méta-modèle graphique basé sur le graphe de propriétés étiquetées (LPG) pour le stockage efficace des données. Deuxièmement, pour rationaliser la découverte et l'accessibilité des techniques d'exploration de processus, nous présentons une architecture orientée services.Pour valider notre méta-modèle graphique, nous avons utilisé deux ensembles de données de processus accessibles au public disponibles à la fois au format CSV et OCEL. Ces ensembles de données ont été essentiels pour évaluer les performances de notre pipeline de requêtes en langage naturel. Nous avons recueilli des requêtes en langage naturel auprès d'utilisateurs externes et en avons généré d'autres à l'aide d'outils de paraphrase. Notre cadre orienté services a été évalué à l'aide de requêtes en langage naturel spécialement conçues pour les descriptions de services d'exploration de processus. De plus, nous avons mené une étude de cas avec des participants externes pour évaluer l'expérience utilisateur et recueillir des commentaires. Nous fournissons publiquement les résultats de l'évaluation pour garantir la reproductibilité dans le domaine étudié
Business processes (BP) are the foundational pillars of organizations, encapsulating a range of structured activities aimed at fulfilling distinct organizational objectives. These processes, characterized by a plethora of tasks, interactions, and workflows, offer a structured methodology for overseeing crucial operations across diverse sectors. A pivotal insight for organizations has been the discernment of the profound value inherent in the data produced during these processes. Process analysis, a specialized discipline, ventures into these data logs, facilitating a deeper comprehension and enhancement of BPs. This analysis can be categorized into two perspectives: instance-level, which focuses on individual process executions, and process-level, which examines the overarching process.However, applying process analysis in practice poses challenges for users, involving the need to access data, navigate low-level APIs, and employ tool-dependent methods. Real-world application often encounters complexities and user-centric obstacles.Specifically, instance-level analysis demands users to access stored process execution data, a task that can be intricate for business professionals due to the requirement of mastering complex query languages like SQL and CYPHER. Conversely, process-level analysis of process data involves the utilization of methods and algorithms that harness process execution data extracted from information systems. These methodologies collectively fall under the umbrella of process mining techniques. The application of process mining confronts analysts with the intricate task of method selection, which involves sifting through unstructured method descriptions. Additionally, the application of process mining methods depends on specific tools and necessitates a certain level of technical expertise.To address these challenges, this thesis introduces AI-driven solutions, with a focus on integrating cognitive capabilities into process analysis to facilitate analysis tasks at both the instance level and the process level for all users. The primary objectives are twofold: Firstly, to enhance the accessibility of process execution data by creating an interface capable of automatically constructing the corresponding database query from natural language. This is complemented by proposing a suitable storage technique and query language that the interface should be designed around. In this regard, we introduce a graph metamodel based on Labeled Property Graph (LPG) for efficient data storage. Secondly, to streamline the discovery and accessibility of process mining techniques, we present a service-oriented architecture. This architecture comprises three core components: an LPG meta-model detailing process mining methods, a service-oriented REST API design tailored for these methods, and a component adept at matching user requirements expressed in natural language with appropriate services.For the validation of our graph metamodel, we utilized two publicly accessible process datasets available in both CSV and OCEL formats. These datasets were instrumental in evaluating the performance of our NL querying pipeline. We gathered NL queries from external users and produced additional ones through paraphrasing tools. Our service-oriented framework underwent an assessment using NL queries specifically designed for process mining service descriptions. Additionally, we carried out a use case study with external participants to evaluate user experience and to gather feedback. We publically provide the evaluation results to ensure reproducibility in the studied area
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Cruz, Lívia Almada. "Consultas kNN em redes dependentes do tempo." reponame:Repositório Institucional da UFC, 2013. http://www.repositorio.ufc.br/handle/riufc/18495.

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CRUZ, Lívia Almada. Consultas kNN em redes dependentes do tempo. 2013. 75 f. Dissertação (Mestrado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2013.
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In this dissertation we study the problem of processing k-nearest neighbours (kNN)queries in road networks considering the history of traffic conditions, in particular the case where the speed of moving objects is time-dependent. For instance, given that the user is at a given location at a certain time, the query returns the k points of interest (e.g., gas stations) that can be reached in the minimum amount of time. Previous solutions to answer kNN queries and others common queries in road networks do not work when the moving speed in each road is not constant. Building efficient and correct approaches and algorithms and storage and access schemes for processing these queries is a challenge because graph properties considered in static networks do not hold in the time dependent case. Our approach uses the well-known A∗ search algorithm by applying incremental network expansion and pruning unpromising vertices. The goal is reduce the percentage of network assessed in the search. To support the algorithm execution, we propose a storage and access method for time-dependent networks. We discuss the design and correctness of our algorithm and present experimental results that show the efficiency and effectiveness of our solution.
Nesta dissertação foi estudado o problema de processar consultas kNN em redes de rodovias considerando o histórico das condições de tráfego, em particular o caso onde a velocidade dos objetos móveis depende do tempo. Dado que um usuário está em uma dada localização e em um determinado instante de tempo, a consulta retorna os k pontos de interesse (por exemplo, postos de gasolina) que podem ser alcançados em uma quantidade de tempo mínima considerando condições históricas de tráfego. Soluções anteriores para consultas kNN e outras consultas comuns em redes de rodovia estáticas não funcionam quando o custo das arestas (tempo de viagem) é dependente do tempo. A construção de estratégias e algoritmos eficientes e corretos, e métodos de armazenamento e acesso para o processamento destas consultas é um desafio desde que algumas das propriedades de grafos comumente supostas em estratégias para redes estáticas não se mantêm para redes dependentes do tempo. O método proposto aplica uma busca A∗ à medida que vai, de maneira incremental, explorando a rede. O objetivo do método é reduzir o percentual da rede avaliado na busca. Para dar suporte à execução do algoritmo, foi também proposto um método para armazenamento e acesso para redes dependentes do tempo. A construção e a corretude do algoritmo são discutidas e são apresentados resultados experimentais com dados reais e sintéticos que mostram a eficiência da solução.
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Cruz, LÃvia Almada. "Consultas kNN em redes dependentes do tempo." Universidade Federal do CearÃ, 2013. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=10343.

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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior
Nesta dissertaÃÃo foi estudado o problema de processar consultas kNN em redes de rodovias considerando o histÃrico das condiÃÃes de trÃfego, em particular o caso onde a velocidade dos objetos mÃveis depende do tempo. Dado que um usuÃrio està em uma dada localizaÃÃo e em um determinado instante de tempo, a consulta retorna os k pontos de interesse (por exemplo, postos de gasolina) que podem ser alcanÃados em uma quantidade de tempo mÃnima considerando condiÃÃes histÃricas de trÃfego. SoluÃÃes anteriores para consultas kNN e outras consultas comuns em redes de rodovia estÃticas nÃo funcionam quando o custo das arestas (tempo de viagem) à dependente do tempo. A construÃÃo de estratÃgias e algoritmos eficientes e corretos, e mÃtodos de armazenamento e acesso para o processamento destas consultas à um desafio desde que algumas das propriedades de grafos comumente supostas em estratÃgias para redes estÃticas nÃo se mantÃm para redes dependentes do tempo. O mÃtodo proposto aplica uma busca A∗ à medida que vai, de maneira incremental, explorando a rede. O objetivo do mÃtodo à reduzir o percentual da rede avaliado na busca. Para dar suporte à execuÃÃo do algoritmo, foi tambÃm proposto um mÃtodo para armazenamento e acesso para redes dependentes do tempo. A construÃÃo e a corretude do algoritmo sÃo discutidas e sÃo apresentados resultados experimentais com dados reais e sintÃticos que mostram a eficiÃncia da soluÃÃo.
In this dissertation we study the problem of processing k-nearest neighbours (kNN)queries in road networks considering the history of traffic conditions, in particular the case where the speed of moving objects is time-dependent. For instance, given that the user is at a given location at a certain time, the query returns the k points of interest (e.g., gas stations) that can be reached in the minimum amount of time. Previous solutions to answer kNN queries and others common queries in road networks do not work when the moving speed in each road is not constant. Building efficient and correct approaches and algorithms and storage and access schemes for processing these queries is a challenge because graph properties considered in static networks do not hold in the time dependent case. Our approach uses the well-known A∗ search algorithm by applying incremental network expansion and pruning unpromising vertices. The goal is reduce the percentage of network assessed in the search. To support the algorithm execution, we propose a storage and access method for time-dependent networks. We discuss the design and correctness of our algorithm and present experimental results that show the efficiency and effectiveness of our solution.
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Baraldi, Andrea. "Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations." Tesi di dottorato, 2017. http://www.fedoa.unina.it/11724/1/TesiDottorato_Unina_XXIX_AB_EO-IU4SQ_v4_RGB_10April2017.pdf.

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By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A  B. This doctoral project moved from the working hypothesis that SCBIR  computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding  EO image understanding (EO-IU) in operating mode, synonym of GEOSS  ESA EO Level 2 product  human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations.
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"Querying early product chemistry in a complex process: A cold molecular beam approach for triglyceride pyrolysis: Cold molecular beam study of Triglycerides pyrolysis chemistry." Tulane University, 2020.

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archives@tulane.edu
A cold molecular beam approach has been pioneered to investigate the pyrolysis reactions of triglycerides (TGs) as a function of temperature. Traditionally, an established repertoire of laser techniques is utilized for multiple species present, which has been extensively used for a detailed study of specifically targeted species that are often novel and reactive. Instead, we have applied these methods for the mass characterization of numerous product species as they appeared. Unlike traditional batch reactor studies of pyrolysis, where terminal products are identified and characterized generally using GC/MS methods, herein, product analysis was conducted in real time. Experiments were performed by recording mass spectra as a function of increasing sample temperature. For clearer results and interpretation, most studies employed model TGs containing a single fatty acid, such as oleic or stearic acid. Soft photoionization was conducted using 118 and 266 nm laser-based pulses. Time-of-flight mass spectroscopy (TOF-MS) was conducted after each photoionization pulse. Several novel direct observations include 1) the observation of initial cracking temperatures and the formation of non-aromatic and aromatic products; 2) the determination of key factors for pyrolysis—fatty acid detachment from the glycerol backbone and subsequent fatty acid pyrolysis; 3) the growth of C6 and C7 fragments as an important precursor for following association reactions. The use of 266 nm pulses exclusively facilitated the sensitive and selective photoionization of aromatic products and, thus, the thorough examination of the evolving aromatic products. Unlike the batch reactor studies of terminal products, the molecular beam studies of aromatic products revealed the evolution to a small number of selective and relatively massive polycyclic aromatic hydrocarbons (PAH). It is deduced that in a batch reactor, these undetected products ultimately lead to solids and tars that are difficult to analyze. Our investigation revealed that hydrogen addition showed some effectiveness in inhibiting formation of large
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Ibrahi M. Alhroob
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Fellmann, Michael. "Semantic Process Engineering – Konzeption und Realisierung eines Werkzeugs zur semantischen Prozessmodellierung." Doctoral thesis, 2013. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2013102311711.

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In der Geschäftsprozessmodellierung haben sich semiformale, grafische Darstellungen etabliert. Die Bezeichnung der Elemente in diesen Modellen ist dabei an betriebswirtschaftliche Fachtermini angelehnt und erfolgt mit Hilfe der natürlichen Sprache, die jedoch Interpretationsspielräume mit sich bringt. Die Semantik der einzelnen Modellelemente ist somit für Menschen und Maschinen nicht eindeutig interpretierbar. In der vorliegenden Dissertation erfolgt daher die Konzeption und Realisierung einer semantischen Prozessmodellierung, die die Verknüpfung der semiformalen Prozessmodellierung mit formalen Begriffssystemen (Ontologien) gestaltet und werkzeugtechnisch unterstützt. Durch diese Verknüpfung wird die Semantik der einzelnen Modellelemente um eine eindeutige und maschinell verarbeitbare Semantik erweitert. Hierdurch können die mit formalen Ontologien möglichen Schlussfolgerungen angewendet werden, um etwa bei der Suche in Modellbeständen oder der Korrektheitsprüfung genauere oder vollständigere Ergebnisse zu erhalten. Im Ergebnis werden somit die im Bereich der Informatik und Künstlichen Intelligenz etablierten Ansätze der Wissensrepräsentation, insbesondere der Beschreibungslogik, in die fachlichen Prozessmodellierung eingebettet. Die Erprobung des Konzepts erfolgt über eine prototypische Implementierung, die einerseits die technische Umsetzbarkeit zeigt, andererseits auch für ein Laborexperiment zur Evaluation genutzt wurde.

Books on the topic "Process querying":

1

Polyvyanyy, Artem, ed. Process Querying Methods. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92875-9.

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Osei-Bryson, Kweku-Muata, and Corlane Barclay. Knowledge discovery process and methods to enhance organizational performance. Boca Raton, FL: CRC Press, Taylor & Francis Group, 2015.

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Polyvyanyy, Artem. Process Querying Methods. Springer International Publishing AG, 2022.

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Benatallah, Boualem, Seyed-Mehdi-Reza Beheshti, Sherif Sakr, Daniela Grigori, Hamid Reza Motahari-Nezhad, Moshe Chai Barukh, Ahmed Gater, and Seung Hwan Ryu. Process Analytics: Concepts and Techniques for Querying and Analyzing Process Data. Springer, 2016.

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Benatallah, Boualem, Seyed-Mehdi-Reza Beheshti, Sherif Sakr, Daniela Grigori, Hamid Reza Motahari-Nezhad, Moshe Chai Barukh, Ahmed Gater, and Seung Hwan Ryu. Process Analytics: Concepts and Techniques for Querying and Analyzing Process Data. Springer, 2018.

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Benatallah, Boualem, Seyed-Mehdi-Reza Beheshti, Sherif Sakr, Daniela Grigori, and Hamid Reza Motahari-Nezhad. Process Analytics: Concepts and Techniques for Querying and Analyzing Process Data. Springer London, Limited, 2016.

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Osei-Bryson, Kweku-Muata, and Corlane Barclay. Knowledge Discovery Process and Methods to Enhance Organizational Performance. Taylor & Francis Group, 2023.

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Book chapters on the topic "Process querying":

1

Schoknecht, Andreas, Tom Thaler, Ralf Laue, Peter Fettke, and Andreas Oberweis. "Process Model Similarity Techniques for Process Querying." In Process Querying Methods, 459–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_16.

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Polyvyanyy, Artem. "Process Query Language." In Process Querying Methods, 313–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_11.

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Polyvyanyy, Artem. "Process Query Language." In Process Querying Methods, 313–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_11.

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Álvarez, Jose Miguel Pérez, Antonio Cancela Díaz, Luisa Parody, Antonia M. Reina Quintero, and María Teresa Gómez-López. "Process Instance Query Language and the Process Querying Framework." In Process Querying Methods, 85–111. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_4.

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Álvarez, Jose Miguel Pérez, Antonio Cancela Díaz, Luisa Parody, Antonia M. Reina Quintero, and María Teresa Gómez-López. "Process Instance Query Language and the Process Querying Framework." In Process Querying Methods, 85–111. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_4.

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Vogelgesang, Thomas, Jessica Ambrosy, David Becher, Robert Seilbeck, Jerome Geyer-Klingeberg, and Martin Klenk. "Celonis PQL: A Query Language for Process Mining." In Process Querying Methods, 377–408. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_13.

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AbstractProcess mining studies data-driven methods to discover, enhance, and monitor business processes by gathering knowledge from event logs recorded by modern IT systems. To gain valuable process insights, it is essential for process mining users to formalize their process questions as executable queries. For this purpose, we present the Celonis Process Query Language (Celonis PQL), which is a domain-specific language tailored toward a special process data model and designed for business users. It translates process-related business questions into queries and executes them on a custom-built query engine. Celonis PQL covers a broad set of more than 150 operators, ranging from process-specific functions to machine learning and mathematical operators. Its syntax is inspired by SQL, but specialized for process-related queries. In addition, we present practical use cases and real-world applications, which demonstrate the expressiveness of the language and how business users can apply it to discover, enhance, and monitor business processes. The maturity and feasibility of Celonis PQL is shown by thousands of users from different industries, who apply it to various process types and huge amounts of event data every day.
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Di Francescomarino, Chiara, and Paolo Tonella. "The BPMN Visual Query Language and Process Querying Framework." In Process Querying Methods, 181–218. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_7.

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Vogelgesang, Thomas, Jessica Ambrosy, David Becher, Robert Seilbeck, Jerome Geyer-Klingeberg, and Martin Klenk. "Celonis PQL: A Query Language for Process Mining." In Process Querying Methods, 377–408. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_13.

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AbstractProcess mining studies data-driven methods to discover, enhance, and monitor business processes by gathering knowledge from event logs recorded by modern IT systems. To gain valuable process insights, it is essential for process mining users to formalize their process questions as executable queries. For this purpose, we present the Celonis Process Query Language (Celonis PQL), which is a domain-specific language tailored toward a special process data model and designed for business users. It translates process-related business questions into queries and executes them on a custom-built query engine. Celonis PQL covers a broad set of more than 150 operators, ranging from process-specific functions to machine learning and mathematical operators. Its syntax is inspired by SQL, but specialized for process-related queries. In addition, we present practical use cases and real-world applications, which demonstrate the expressiveness of the language and how business users can apply it to discover, enhance, and monitor business processes. The maturity and feasibility of Celonis PQL is shown by thousands of users from different industries, who apply it to various process types and huge amounts of event data every day.
9

Di Francescomarino, Chiara, and Paolo Tonella. "The BPMN Visual Query Language and Process Querying Framework." In Process Querying Methods, 181–218. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_7.

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Delfmann, Patrick, Dennis M. Riehle, Steffen Höhenberger, Carl Corea, and Christoph Drodt. "The Diagramed Model Query Language 2.0: Design, Implementation, and Evaluation." In Process Querying Methods, 115–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92875-9_5.

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Conference papers on the topic "Process querying":

1

Awad, Ahmed, Artem Polyvyanyy, and Mathias Weske. "Semantic Querying of Business Process Models." In 2008 12th International IEEE Enterprise Distributed Object Computing Conference (EDOC). IEEE, 2008. http://dx.doi.org/10.1109/edoc.2008.11.

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Störrle, Harald, and Vlad Acretoaie. "Querying business process models with VMQL." In the 5th ACM SIGCHI Annual International Workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2492437.2492441.

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Yongsiriwit, Karn, Nguyen Ngoc Chan, and Walid Gaaloul. "Log-Based Process Fragment Querying to Support Process Design." In 2015 48th Hawaii International Conference on System Sciences (HICSS). IEEE, 2015. http://dx.doi.org/10.1109/hicss.2015.493.

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Stanchev, Lubomir. "Querying Incomplete Information Using Bag Relational Algebra." In 2010 Second International Conference on Information, Process, and Knowledge Management (eKNOW). IEEE, 2010. http://dx.doi.org/10.1109/eknow.2010.9.

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Andriamarozakaniaina, Tahiry, Véronique Gaildrat, and Matthieu Pouget. "Process of indexing and querying for theatrical texts." In the 2008 International Conference in Advances. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1501750.1501870.

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Han, Xue, Lianxue Hu, Jaydeep Sen, Yabin Dang, Buyu Gao, Vatche Isahagian, Chuan Lei, et al. "Bootstrapping Natural Language Querying on Process Automation Data." In 2020 IEEE International Conference on Services Computing (SCC). IEEE, 2020. http://dx.doi.org/10.1109/scc49832.2020.00030.

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Kobeissi, Meriana, Nour Assy, Walid Gaaloul, Bruno Defude, and Bassem Haidar. "An Intent-Based Natural Language Interface for Querying Process Execution Data." In 2021 3rd International Conference on Process Mining (ICPM). IEEE, 2021. http://dx.doi.org/10.1109/icpm53251.2021.9576850.

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Sakr, Sherif, and Ahmed Awad. "A framework for querying graph-based business process models." In the 19th international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1772690.1772906.

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Kammerer, Klaus, Rudiger Pryss, and Manfred Reichert. "Context-Aware Querying and Injection of Process Fragments in Process-Aware Information Systems." In 2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC). IEEE, 2020. http://dx.doi.org/10.1109/edoc49727.2020.00022.

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Song, Liang, Jianmin Wang, Lijie Wen, Wenxing Wang, Shijie Tan, and Hui Kong. "Querying Process Models Based on the Temporal Relations between Tasks." In 2011 15th IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW). IEEE, 2011. http://dx.doi.org/10.1109/edocw.2011.12.

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Reports on the topic "Process querying":

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Bourgaux, Camille, and Anni-Yasmin Turhan. Temporal Query Answering in DL-Lite over Inconsistent Data. Technische Universität Dresden, 2017. http://dx.doi.org/10.25368/2022.236.

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In ontology-based systems that process data stemming from different sources and that is received over time, as in context-aware systems, reasoning needs to cope with the temporal dimension and should be resilient against inconsistencies in the data. Motivated by such settings, this paper addresses the problem of handling inconsistent data in a temporal version of ontology-based query answering. We consider a recently proposed temporal query language that combines conjunctive queries with operators of propositional linear temporal logic and extend to this setting three inconsistency-tolerant semantics that have been introduced for querying inconsistent description logic knowledge bases. We investigate their complexity for DL-LiteR temporal knowledge bases, and furthermore complete the picture for the consistent case.
2

Bond, W., Maria Seale, and Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), April 2022. http://dx.doi.org/10.21079/11681/43886.

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Data management systems impose structure on data via a static representation schema or data structure. Information from the data is extracted by executing queries based on predefined operators. This paradigm restricts the searchability of the data to concepts and relationships that are known or assumed to exist among the objects. While this is an effective and efficient means of retrieving simple information, we propose that such a structure severely limits the ability to derive breakthrough knowledge that exists in data under the guise of “unknown unknowns.” A dynamic system will alleviate this dependence, allowing theoretically infinite projections of the data to reveal discoverable relationships that are hidden by traditional use case-driven, static query systems. In this paper, we propose a framework for a data-responsive query algebra based on a dynamic hyperbolic surface model. Such a model could provide more intuitive access to analytics and insights from massive, aggregated datasets than existing methods. This model will significantly alter the means of addressing the underlying data by representing it as an arrangement on a dynamic, hyperbolic plane. Consequently, querying the data can be viewed as a process similar to quantum annealing, in terms of characterizing data representation as an energy minimization problem with numerous minima.
3

Koopmann, Patrick. Ontology-Mediated Query Answering for Probabilistic Temporal Data with EL Ontologies (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.242.

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Especially in the field of stream reasoning, there is an increased interest in reasoning about temporal data in order to detect situations of interest or complex events. Ontologies have been proved a useful way to infer missing information from incomplete data, or simply to allow for a higher order vocabulary to be used in the event descriptions. Motivated by this, ontology-based temporal query answering has been proposed as a means for the recognition of situations and complex events. But often, the data to be processed do not only contain temporal information, but also probabilistic information, for example because of uncertain sensor measurements. While there has been a plethora of research on ontologybased temporal query answering, only little is known so far about querying temporal probabilistic data using ontologies. This work addresses this problem by introducing a temporal query language that extends a well-investigated temporal query language with probability operators, and investigating the complexity of answering queries using this query language together with ontologies formulated in the description logic EL.

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