Intelligent Knowledge Lakes: The Age of Artificial Intelligence and Big Data

Beheshti, Amin; Benatallah, Boualem; Sheng, Quan Z.; Schiliro, Francesco

doi:10.1007/978-981-15-3281-8_3

Cited by 24 publications

(14 citation statements)

References 20 publications

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“…The service helps eliminate manual errors related to strict coding data import processes. Automation also brings complex analytical skills when capturing new data (Beheshti et al, 2020).…”

Section: Challenges Of Data Import and Data Lake-as-a-servicementioning

confidence: 99%

Strategic Growth of Everything-as-a-Service (XaaS) Business Model Transformation

Chimakurthi

2021

Eng. int. (Dhaka)

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The study presents the XaaS architecture and explores how this business model has strategically developed and how it has revolutionized the whole business industry. The study indicates how this particular business model allows companies to enhance the range of advantages of Internet Cloud model applications, particularly when it deals with process of media processing and customization of users. Its huge benefits with negligible disadvantages are mentioned as part of the presented research. This study will help to understand the services of XaaS model, challenges experiencing by it and its future opportunities. This will also assists new scholars of field to build a better understanding and better prospects regarding XaaS in future.

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“…The service helps eliminate manual errors related to strict coding data import processes. Automation also brings complex analytical skills when capturing new data (Beheshti et al, 2020).…”

Section: Challenges Of Data Import and Data Lake-as-a-servicementioning

confidence: 99%

Strategic Growth of Everything-as-a-Service (XaaS) Business Model Transformation

Chimakurthi

2021

Eng. int. (Dhaka)

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“…The domain knowledge can play an important role as it can help to better understand, categorize, contextualize and analyze the information in a particular domain. In our previous work, we introduced Intelligent Knowledge Lakes [15] to facilitate linking Artificial Intelligence (AI) and Data Analytics; to enable AIenabled applications to learn from the domain knowledge (e.g., in retail, finance, tourism, health, etc.) and use this knowledge to automate business processes and develop cognitive assistance for facilitating the knowledge intensive processes or generating new rules for future business analytics.…”

Section: Domain-specific Recommendersmentioning

confidence: 99%

“…As discussed in Section 3.1, Knowledge Lakes are used to facilitate the content and context analytics for the RSs, as they contain virtually inexhaustible amounts of both data and contextualized data that is readily made available anytime to anyone authorized to perform analytical activities. Intelligent Knowledge Lakes [15] to facilitate linking Artificial Intelligence (AI) and Data Analytics, to enable learning from domain expert's experience, and use this knowledge to annotate the contextualized information stored in a knowledge lake. The goal is to enable RSs to learn from contextualized data and use them to develop cognitive assistance for RSs' users for: facilitating the recommendation processes and learning from the users' cognition over time (by generating new rules for future predictions).…”

Section: Domain Knowledgementioning

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]. The goal is to leverage techniques such as reinforcement learning [16] to strengthen desirable and accurate recommendations and minimize or eliminate undesirable recommendations; and • cognition-driven-which enables understanding the 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

Self Cite

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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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“…In this section, we will first discuss the key terms related to this research including customer segmentation, classification, and clustering. Then by analyzing the related works, we will discuss the researches on intelligent methods [6] and machine learning models in the current works in customer segmentation in the literature. They include different cases of using supervised and unsupervised customer segmentation.…”

Section: Background and State-of-the-artmentioning

confidence: 99%

Intelligent Vector-based Customer Segmentation in the Banking Industry

Mousaeirad

2020

Preprint

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Customer Segmentation is the process of dividing customers into groups based on common characteristics. An intelligent Customer Segmentation will not only enable an organization to effectively allocate marketing resources (e.g., Recommender Systems in the Banking sector) but also it will enable identifying the customer cohorts that are most likely to benefit from a specific policy (e.g., to discover diverse patient groups in the Health sector). While there has been a significant improvement in approaches to Customer Segmentation, the main challenge remains to be the understanding of the reasons behind the segmentation need. This task is challenging as it is subjective and depends on the goal of segmentation as well as the analyst's perspective. To address this challenge, in this paper, we present an intelligent vector-based customer segmentation approach. The proposed approach will leverage feature engineering to enable analysts to identify important features (from a pool of features such as demographics, geography, psychographics, behavioral, and more) and feed them into a neural embedding framework named Cus-tomer2Vec. The Customer2Vec combines the neural network classification and clustering methods as supervised and unsupervised learning techniques to embed the customer vector. We adopt a typical scenario in the Banking Sector to highlight how Customer2Vec significantly improves the quality of the segmentation and detecting customer similarities.

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Intelligent Knowledge Lakes: The Age of Artificial Intelligence and Big Data

Cited by 24 publications

References 20 publications

Strategic Growth of Everything-as-a-Service (XaaS) Business Model Transformation

Strategic Growth of Everything-as-a-Service (XaaS) Business Model Transformation

Towards Cognitive Recommender Systems

Intelligent Vector-based Customer Segmentation in the Banking Industry

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