Scalable graph-based OLAP analytics over process execution data

Beheshti, Amin; Benatallah, Boualem; Motahari-Nezhad, Hamid Reza

doi:10.1007/s10619-014-7171-9

Cited by 55 publications

(38 citation statements)

References 80 publications

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“…Figure illustrates the main components of ProcessAtlas architecture in the context of the motivating scenario, where the main entities include hospital, department, lab, patient, treatment, case, event, artifacts/documents, diagnosis, and actors such as doctors and nurses. Based on the type of connections between these entities in the log, it is possible to contextualize a set of relationships among these entities such as() activity‐based relationships (eg, actor‐triggered event), time‐based relationships (eg, event happened before event), and content‐based relationships (eg, artifact part of artifact). Figure illustrates a sample process graph of possible relationships between entities in this scenario.…”

Section: Processatlas: Overview and Motivating Scenariomentioning

confidence: 99%

“… This service will use the process graph to discover process instances. We use our previous work() to analyze the process graph and discover events that are correlated into process instances. In particular, we use correlation conditions as a binary predicate defined on the attributes of event payload that allows identifying whether 2 or more events in the process graph are potentially related to the same execution instance of a process.…”

Section: Process Data Lake Servicesmentioning

confidence: 99%

“…A path node is a place holder for a set of paths (ie, transitive relationship between 2 entities) in the process graph. The relationships among entities can be codified using regular expressions in which alphabets are the nodes and edges from the graph.…”

Section: Domain‐specific Model For Representing Process Knowledgementioning

confidence: 99%

“…Note that an entity is related to other entities by time (time‐based), content (content‐based), or activity (activity‐based). () (1) Time‐based Relationships : Time is the relationship that orders events, for example, e v e n t A happened before e v e n t B . A timestamp is attached to an activity where every timestamp value is unique and accurately represents an instant in time.…”

Section: Domain‐specific Model For Representing Process Knowledgementioning

confidence: 99%

“…For each of these queries, we provide an analysis service. Scalable Process Query Services: We provide efficient mapping and execution of process‐level queries into graph‐level queries by using scalable process query services to deal with the process data size growth and with the infrastructure's scale. To achieve this and to support a scalable and efficient analysis over process data, we extend our previous work (a graph query engine for organizing and querying process data()) to store and query large process graphs using Apache Hadoop solution . We implement scalable process query services to translate domain‐specific queries into large‐scale MapReduce‐based queries to speed up data processing and to scale up with data volume.…”

Section: Introductionmentioning

confidence: 99%

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ProcessAtlas: A scalable and extensible platform for business process analytics

2018

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In today's knowledge-, service-, and cloud-based economy, an overwhelming amount of business-related data are being generated at a fast rate daily from a wide range of sources. These data increasingly show all the typical properties of big data: wide physical distribution, diversity of formats, nonstandard data models, and independently managed and heterogeneous semantics. In this context, there is a need for new scalable and process-aware services for querying, exploration, and analysis of process data in the enterprise because (1) process data analysis services should be capable of processing and querying large amount of data effectively and efficiently and, therefore, have to be able to scale well with the infrastructure's scale and (2) the querying services need to enable users to express their data analysis and querying needs using process-aware abstractions rather than other lower-level abstractions. In this paper, we introduce Proces-sAtlas, ie, an extensible large-scale process data querying and analysis platform for analyzing process data in the enterprise. The ProcessAtlas platform offers an extensible architecture by adopting a service-based model so that new analytical services can be plugged into the platform. In ProcessAtlas, we present a domain-specific model for representing process knowledge, ie, process-level entities, abstractions, and the relationships among them modeled as graphs. We provide services for discovering, extracting, and analyzing process data. We provide efficient mapping and execution of process-level queries into graph-level queries by using scalable process query services to deal with the process data size growth and with the infrastructure's scale. We have implemented ProcessAtlas as a MapReduce-based prototype and report on experiments performed on both synthetic and real-world datasets. goal is to understand how a BP is performed and to identify opportunities for improvement. 2 However, the wide-scale automation in enterprises has led to having BPs implemented over many systems. Therefore, answering questions such as "What are the dependencies between 2 different cases? What is the typical path for a case and how much time and resources can be spent in processing it? What is the story behind the file number #756.1: where is the origin of this file? How it evolved over time? Who was involved in updating this file?" becomes difficult at best.The main barrier for answering questions like those aforementioned is that, in today's knowledge-, service-, and cloud-based economy, the information about process execution is scattered across several systems and data sources. Consequently, process logs increasingly come to show all typical properties of the big data 3 : wide physical distribution, diversity of formats, nonstandard data models, and independently managed and heterogeneous semantics. We use the term Process Data to refer to such large hybrid collections of heterogeneous and partially unstructured process-related execution data. Digitalization of business artifacts (eg, doc...

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Section: Processatlas: Overview and Motivating Scenariomentioning

confidence: 99%

Section: Process Data Lake Servicesmentioning

confidence: 99%

Section: Domain‐specific Model For Representing Process Knowledgementioning

confidence: 99%

Section: Domain‐specific Model For Representing Process Knowledgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ProcessAtlas: A scalable and extensible platform for business process analytics

2018

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An improved join‐free snowflake schema for ETL and OLAP of data warehouse

Wang

Zhao

Fan

et al. 2019

Concurrency and Computation

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The emergence of big data makes more and more enterprise change data management strategy, from simple data storage to OLAP query analysis; meanwhile, NoSQL-based data warehouse receive more increasing attention than traditional SQL-based database. By improving the JFSS model for ETL, this paper proposes the uniform distribution code (UDC), model identification code (MIC), standard dimension code (SDC), and attribute dimensional code (ADC); defines the data storage format of ; and identifies the extraction, transformation, and loading strategies of data warehouse. Several experiments are carried out to analyze single record and range record queries as typical OLAP based on Hadoop database (HBase). The results show the proposed scheme can provide lower overhead than the traditional SQL-based database while facilitating the scope and flexibility of data warehouse services.

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