LabBook: Metadata-driven social collaborative data analysis

Kandogan, Eser; Roth, Mary T.; Schwarz, Peter; Hui, Joshua; Terrizzano, Ignacio; Christodoulakis, Christina; Miller, Renée J.

doi:10.1109/bigdata.2015.7363784

Cited by 40 publications

(24 citation statements)

References 3 publications

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“…Our method can also be applied on script provenance by segmenting within and summarizing across evolving run graphs. Data Science Lifecycle Management: Recently, there is emerging interest in developing systems for managing different aspects in the modeling lifecycle, such as building modeling lifecycle platforms [55], accelerating iterative modeling process [56], managing developed models [5], [8], organizing lifecycle provenance and metadata [1], [2], [4], auto-selecting models [57], hosting pipelines and discovering reference models [58], [59], and assisting collaboration [60]. Issues of querying evolving and verbose provenance effectively are not considered in that work.…”

Section: Related Workmentioning

confidence: 99%

Understanding Data Science Lifecycle Provenance via Graph Segmentation and Summarization

Miao

Deshpande

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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Along with the prosperous data science activities, the importance of provenance during data science project lifecycle is recognized and discussed in recent data science systems research. Increasingly modern data science platforms today have nonintrusive and extensible provenance ingestion mechanisms to collect rich provenance and context information, handle modifications to the same file using distinguishable versions, and use graph data models (e.g., property graphs) and query languages (e.g., Cypher) to represent and manipulate the stored provenance/context information. Due to the schema-later nature of the metadata, multiple versions of the same files, and unfamiliar artifacts introduced by team members, the "provenance graph" is verbose and evolving, and hard to understand; using standard graph query model, it is difficult to compose queries and utilize this valuable information.In this paper, we propose two high-level graph query operators to address the verboseness and evolving nature of such provenance graphs. First, we introduce a graph segmentation operator, which queries the retrospective provenance between a set of source vertices and a set of destination vertices via flexible boundary criteria to help users get insight about the derivation relationships among those vertices. We show the semantics of such a query in terms of a context-free grammar, and develop efficient algorithms that run orders of magnitude faster than state-of-the-art. Second, we propose a graph summarization operator that combines similar segments together to query prospective provenance of the underlying project. The operator allows tuning the summary by ignoring vertex details and characterizing local structures, and ensures the provenance meaning using path constraints. We show the optimal summary problem is PSPACE-complete and develop effective approximation algorithms. The operators are implemented on top of a property graph backend. We evaluate our query methods extensively and show the effectiveness and efficiency of the proposed methods.

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Section: Related Workmentioning

confidence: 99%

Understanding Data Science Lifecycle Provenance via Graph Segmentation and Summarization

Miao

Deshpande

2019

2019 IEEE 35th International Conference on Data Engineering (ICDE)

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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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“…In many application domains, including sciences, there is a strong need to be able to collaborate through sharing of data, information, and knowledge. Data integration technologies and crowdsourcing platforms, such as MiNC [1] and LabBook [15], provide great opportunities in this direction.…”

Section: Related Workmentioning

confidence: 99%

Crowd Sourced Semantic Enrichment (CroSSE) for knowledge driven querying of digital resources

Cavallo¹,

Mauro²,

Pasteris³

et al. 2019

J Intell Inf Syst

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Today, most information sources provide factual, objective knowledge, but they fail to capture personalized contextual knowledge which could be used to enrich the available factual data and contribute to their interpretation, in the context of the knowledge of the user who queries the system. This would require a knowledge framework which can accommodate both objective data and semantic enrichments that capture user provided knowledge associated to the factual data in the database. Unfortunately, most conventional DBMSs lack the flexibilities necessary (a) to prevent the data and metadata, evolve quickly with changing application requirements and (b) to capture user-provided and/or crowdsourced data and knowledge for more effective decision support. In this paper, we present CrowdSourced Semantic Enrichment (CroSSE) knowledge framework which allows traditional databases and semantic enrichment modules to coexist. CroSSE provides a novel Semantically Enriched SQL (SESQL) language to enrich SQL queries with information from a knowledge base containing semantic annotations. We describe CroSSE and SESQL with examples taken from our SmartGround EU project.

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LabBook: Metadata-driven social collaborative data analysis

Cited by 40 publications

References 3 publications

Understanding Data Science Lifecycle Provenance via Graph Segmentation and Summarization

Understanding Data Science Lifecycle Provenance via Graph Segmentation and Summarization

A Domain Ontology and Software Platform for Collaborative Personal Data Analytics

Crowd Sourced Semantic Enrichment (CroSSE) for knowledge driven querying of digital resources

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