The future is big graphs

Sakr, Sherif; Bonifati, Angela; Voigt, Hannes; Iosup, Alexandru; Ammar, Khaled; Angles, Renzo; Aref, Walid G.; Arenas, Marcelo; Besta, Maciej; Boncz, Peter; Daudjee, Khuzaima; Valle, Emanuele Della; Dumbrava, Stefania; Hartig, Olaf; Haslhofer, Bernhard; Hegeman, Tim; Hidders, Jan; Hose, Katja; Iamnitchi, Adriana; Kalavri, Vasiliki; Hugo, Kapp,; Martens, Wim; Özsu, M. Tamer; Peukert, Eric; Plantikow, Stefan; Ragab, Mohamed; Ripeanu, Matei; Salihoğlu, Semih; Schulz, Christian; Selmer, Petra; Sequeda, Juan F.; Shinavier, Joshua; Szárnyas, Gábor; Tommasini, Riccardo; Tumeo, Antonino; Uta, Alexandru; Vărbănescu, Ana Lucia; Wu, Hsiang‐Yun; Yakovets, Nikolay; Yan, Da; Yoneki, Eiko

doi:10.1145/3434642

Cited by 106 publications

(29 citation statements)

References 29 publications

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“…Vertical partitioning is the typical configuration of the datasets used to build the nextgeneration VLEs. Databases used to store and query e-learning data can be implemented with diverse storage techniques, including graph databases [39], e.g., RDF (Resource Description Framework) triplestores, and relational databases [40].…”

Section: Privacy-preserving Data Mashupmentioning

confidence: 99%

“…Hence, basing our data mashup implementation upon relational databases would have been reasonable. However, the popularity of graph databases is constantly increasing [39]-graph DBMS are 14 times more prevalent than relational DBMSs. On the other hand, opting for general-purpose graph database technology would require revised versions of PPDP algorithms, which is out of our scope.…”

Section: Database Management Technologymentioning

confidence: 99%

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Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

2021

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Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students’ learning. However, data sets involved in mashups may contain personal information of sensitive nature that raises legitimate privacy concerns. Average privacy preservation methods are based on preemptive approaches that limit the published data in a mashup based on access control and authentication schemes. Such limitations may reduce the analytical utility of the data exposed to gain students’ learning insights. In order to reconcile utility and privacy preservation of published data, this research proposes a new data mashup protocol capable of merging and k-anonymizing data sets in cloud-based learning environments without jeopardizing the analytical utility of the information. The implementation of the protocol is based on linked data so that data sets involved in the mashups are semantically described, thereby enabling their combination with relevant educational data sources. The k-anonymized data sets returned by the protocol still retain essential information for supporting general data exploration and statistical analysis tasks. The analytical and empirical evaluation shows that the proposed protocol prevents individuals’ sensitive information from re-identifying.

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Section: Privacy-preserving Data Mashupmentioning

confidence: 99%

Section: Database Management Technologymentioning

confidence: 99%

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

2021

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“…Graph databases are becoming widely used [46]. Many applications require graph-structured data for analysis: they are found in a variety of scientific domains, such as biology and chemistry [29].…”

Section: Introductionmentioning

confidence: 99%

Graph Pattern Matching in GQL and SQL/PGQ

Deutsch¹,

Francis

Green

et al. 2022

Proceedings of the 2022 International Conference on Management of Data

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As graph databases become widespread, JTC1-the committee in joint charge of information technology standards for the International Organization for Standardization (ISO), and International Electrotechnical Commission (IEC)-has approved a project to create GQL, a standard property graph query language. This complements a project to extend SQL with a new part, SQL/PGQ, which specifies how to define graph views over an SQL tabular schema, and to run read-only queries against them.Both projects have been assigned to the ISO/IEC JTC1 SC32 working group for Database Languages, WG3, which continues to maintain and enhance SQL as a whole. This common responsibility helps enforce a policy that the identical core of both PGQ and GQL is a graph pattern matching sub-language, here termed GPML.The WG3 design process is also analyzed by an academic working group, part of the Linked Data Benchmark Council (LDBC), whose task is to produce a formal semantics of these graph data languages, which complements their standard specifications.

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“…Networks are very important to solve problems in decision making and knowledge discovery 3 . Such networks can represent many real-world phenomena and are technically described by graphs, which provide a unifying abstraction by which such real-world networks can be represented, explored, predicted and discovered 4 . Most importantly, such structures help to make complex phenomena re-traceable, transparent, interpretable and thus explainable to human experts.…”

Section: Introductionmentioning

confidence: 99%

Multi-omics disease module detection with an explainable Greedy Decision Forest

Pfeifer

Baniecki

Saranti

et al. 2022

Sci Rep

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Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form of graphs or networks, and its use can improve model performance. We need network-based algorithms that are versatile and applicable in many research areas. In this work, we demonstrate subnetwork detection based on multi-modal node features using a novel Greedy Decision Forest (GDF) with inherent interpretability. The latter will be a crucial factor to retain experts and gain their trust in such algorithms. To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well. Systems biology is a good example of a field in which statistical data-driven machine learning enables the analysis of large amounts of multi-modal biomedical data. This is important to reach the future goal of precision medicine, where the complexity of patients is modeled on a system level to best tailor medical decisions, health practices and therapies to the individual patient. Our proposed explainable approach can help to uncover disease-causing network modules from multi-omics data to better understand complex diseases such as cancer.

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The future is big graphs

Abstract: Ensuring the success of big graph processing for the next decade and beyond.

Cited by 106 publications

References 29 publications

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

Graph Pattern Matching in GQL and SQL/PGQ

Multi-omics disease module detection with an explainable Greedy Decision Forest

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