DataSynapse: A Social Data Curation Foundry

Beheshti, Amin; Benatallah, Boualem; Tabebordbar, Alireza; Motahari-Nezhad, Hamid Reza; Barukh, Moshe Chai; Nouri, Reza

doi:10.1007/s10619-018-7245-1

Cited by 40 publications

(34 citation statements)

References 13 publications

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“…This process scales the dataset to an interval of (−1,1). Here, we use PCA and K-means together because after applying dimensional reduction and normalization, the subspace spanning the principal direction is the same compared to the cluster centroid subspace [ 24 , 25 , 26 , 27 ]. For example, the following figure is an example of PCA analysis using VIRMOTIF on 3000 hepatitis B viruses.…”

Section: Methodsmentioning

confidence: 99%

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Rajaei

Jahanian

Beheshti

et al. 2021

Genes

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Bioinformatics and computational biology have significantly contributed to the generation of vast and important knowledge that can lead to great improvements and advancements in biology and its related fields. Over the past three decades, a wide range of tools and methods have been developed and proposed to enhance performance, diagnosis, and throughput while maintaining feasibility and convenience for users. Here, we propose a new user-friendly comprehensive tool called VIRMOTIF to analyze DNA sequences. VIRMOTIF brings different tools together as one package so that users can perform their analysis as a whole and in one place. VIRMOTIF is able to complete different tasks, including computing the number or probability of motifs appearing in DNA sequences, visualizing data using the matplotlib and heatmap libraries, and clustering data using four different methods, namely K-means, PCA, Mean Shift, and ClusterMap. VIRMOTIF is the only tool with the ability to analyze genomic motifs based on their frequency and representation (D-ratio) in a virus genome.

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Section: Methodsmentioning

confidence: 99%

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Rajaei

Jahanian

Beheshti

et al. 2021

Genes

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“…The explosive growth in the amount of available information has created a potential challenge of curating and analyzing the information in order to find a valuable and useful insight. The process of curating the information and prepare them for analytics is important as the driven knowledge can be a vital asset for organizations and governments [14]. This knowledge may refer to a set of facts, information, and insights which are extracted and curated from raw data [98].…”

Section: Domain-specific Recommendersmentioning

confidence: 99%

“…• data-driven-which enables leveraging Artificial Intelligence and Machine Learning technologies to contextualize the Big Data generated on Open, Private and Social platforms/systems to improve the accuracy of recommendations [14]. The goal is to facilitate the use of content and collaborative filtering, and focus on the shift from statistical modeling to deep learning-based modeling (Deep Learning Recommendation Models) to improve correlations between features and attributes to generate better predictions; • knowledge-driven-which enables mimicking the knowledge of domain experts using crowdsourcing techniques [15].…”

Section: Introductionmentioning

confidence: 99%

“…We present a motivating scenario in finance industry and argue that existing Recommender Systems-(i) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (ii) do not support data capture and analytics around customers' cognitive activities and using it to provide intelligent and time-aware recommendations. (ii) data-driven [14]: which enables leveraging Artificial Intelligence and Machine Learning technologies to understand the Big Data generated on Open, Private and Social platforms/systems to improve the accuracy of recommendations; and (iii) cognition-driven [17]: which enables understanding the end-users personality and Analyze their behaviour and attitude over time.…”

Section: Introductionmentioning

confidence: 99%

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Towards Cognitive Recommender Systems

et al. 2020

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Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations.

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“…For example, if a user retweets a tweet on Twitter, it would be helpful to understand the text of the tweet, whether it contains an image or URL, and the keywords or entities (e.g., people, organisations, locations and products) and topics mentioned. In this context, data curation [144]- [146] (i.e., the task of preparing the raw data for analytics) can help in turning raw data into contextualised data and knowledge. For example, curating a raw tweet from Twitter can tell us if the tweet contains a mention of a person named Barak Obama (using entity extraction and coreference reolution techniques [147]) who was the 44th president of the United States (using linking techniques [148] to link this entity to external knowledge sources such as Wikidata 7 ).…”

Section: Approach Time Complexitymentioning

confidence: 99%

A Survey on Trust Prediction in Online Social Networks

et al. 2020

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Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

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DataSynapse: A Social Data Curation Foundry

Cited by 40 publications

References 13 publications

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Towards Cognitive Recommender Systems

A Survey on Trust Prediction in Online Social Networks

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