Academic literature on the topic 'Process querying'

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<br>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

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.<br>Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-11T18:24:05Z No. of bitstreams: 1 2013_dis_lacruz.pdf: 6954650 bytes, checksum: fbf7280f2f781976bae6e4474c2c16c6 (MD5)<br>Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-20T11:52:58Z (GMT) No. of bitstreams: 1 2013_dis_lacruz.pdf: 6954650 bytes, checksum: fbf7280f2f781976bae6e4474c2c16c6 (MD5)<br>Made available in DSpace on 2016-07-20T11:52:58Z (GMT). No. of bitstreams: 1 2013_dis_lacruz.pdf: 6954650 bytes, checksum: fbf7280f2f781976bae6e4474c2c16c6 (MD5) Previous issue date: 2013<br>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.<br>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.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior<br>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 e&#64257;cientes e corretos, e mÃtodos de armazenamento e acesso para o processamento destas consultas à um desa&#64257;o 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&#8727; 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 e&#64257;ciÃncia da soluÃÃo.<br>In this dissertation we study the problem of processing k-nearest neighbours (kNN)queries in road networks considering the history of traf&#64257;c 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 ef&#64257;cient 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&#8727; 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 ef&#64257;ciency and effectiveness of our solution.

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.

archives@tulane.edu<br>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<br>1<br>Ibrahi M. Alhroob

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.