Collaborative management of a repository of KDD processes

Diamantini, Claudia; Potena, Domenico; Storti, Emanuele

doi:10.1504/ijmso.2014.065428

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…Typically the repository is used to share data, as in systems such as Google Fusion Tables [ 21 ], SQLShare [ 22 ], DataHub [ 23 ] and SeedMe [ 24 ]. It is also possible to collaborate by sharing data analytics tools and workflows, as is the case with RCloud [ 25 ], KDDVM [ 26 , 27 ] and the collaborative big data analytics platform of [ 28 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial

Tuovinen

Smeaton

2022

PLoS ONE

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Personal wellness data collected using wearable devices is a valuable resource, potentially containing knowledge that goes beyond what the device and its the associated software application can tell the user. However, extracting such knowledge from the data requires expertise that an average user cannot be expected to have. To overcome this problem, the data owner could collaborate with a data analysis expert; for such a collaboration to succeed, the collaborators need to be able to find one another, communicate with one another and share datasets and analysis results with one another. In this paper we presents a process model for such collaborations, a domain ontology and software system developed to support the process, and the results of a user trial demonstrating collaborative analysis of sleep data. Unlike existing collaborative data analytics tools, the process and software have been specifically designed with the non-expert data owner in mind, enabling them to control their data and protect their privacy by selecting the data to be shared on a case-by-case basis. Theoretical analysis and empirical results suggest that the process and its implementation are valid as a proof of concept.

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Section: Background and Related Workmentioning

confidence: 99%

Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial

Tuovinen

Smeaton

2022

PLoS ONE

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“…Using the platform, a person who has collected self-tracking data can find a KDD expert with the required skill set, negotiate with the expert to agree on the terms of the collaboration, collaboratively analyze the data and evaluate the results. Collaborative KDD platforms such as KDDVM [5] and LabBook [9] already exist, but to the best of our knowledge there are none that specifically target the sub-domain of personal analytics.…”

Section: Background and Motivationmentioning

confidence: 99%

A Domain Ontology and Software Platform for Collaborative Personal Data Analytics

Tuovinen

Smeaton

2019

Lecture Notes in Computer Science

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Collaborative knowledge discovery is a promising approach by which people with no data analytics expertise could benefit from an analysis of their own personal data by experts. To facilitate effective collaboration between data owners and knowledge discovery experts, we have developed a software platform that uses a domain ontology to represent knowledge relevant to the execution of the collaborative knowledge discovery process. The ontology provides classes representing the main elements of collaborations: collaborators and datasets. Furthermore, the ontology enables the specification of privacy constraints that determine the precise extent to which a given dataset of personal data is shared with a given collaborator. We have developed a client-server software platform that enables users to initiate collaborations, invite experts to join them, create datasets and share them with experts, and create visualisations of data. The collaborations are mediated through the creation, modification and deletion of individuals in the underlying ontology and the propagation of ontology changes to each client connected to the server.

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Behavioral process mining for unstructured processes

2016

Self Cite

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Real world applications provide many examples of unstructured processes, where process execution is mainly driven by contingent decisions taken by the actors, with the result that the process is rarely repeated exactly in the same way. In these cases, traditional Process Discovery techniques, aimed at extracting complete process models from event logs, reveal some limits. In fact, when applied to logs of unstructured processes, Process Discovery techniques usually return complex, "spaghetti-like" models, which usually provide limited support to analysts. As a remedy, in the present work we propose Behavioral Process Mining as an alternative approach to enlighten relevant subprocesses, representing meaningful collaboration work practices. The approach is based on the application of hierarchical graph clustering to the set of instance graphs generated by a process. We also describe a technique for building instance graphs from traces. We assess advantages and limits of the approach on a set of synthetic and real world experiments.

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Collaborative management of a repository of KDD processes

Cited by 4 publications

References 31 publications

Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial

Privacy-aware sharing and collaborative analysis of personal wellness data: Process model, domain ontology, software system and user trial

A Domain Ontology and Software Platform for Collaborative Personal Data Analytics

Behavioral process mining for unstructured processes

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