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📢📜New paper alert! Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions

Anastasija Nikiforova Leave a comment

This post is dedicated to the “Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions” (Ukpabi, D.C., Karjaluoto, H., Botticher, A., Nikiforova, A., Petrescu, D.I., Schindler, P., Valtenbergs, V., Lehmann, L.) paper that just has been published in Futures journal (Elsevier, Q1 in both (1) Business and International Management, (2) Development, (3) Sociology and Political Science) in an open access.

Recently, there has been increasing awareness of the tremendous opportunities inherent in quantum computing. It is expected that the speed and efficiency of quantum computing will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased quantum computing research funding from national and regional governments and private firms. However, critical concerns regarding legal, political, and business-related policies germane to quantum computing adoption exist. Therefore, recently a call for a framework from an interdisciplinary perspective has been made to help an understanding the potential impact of quantum computing on society, which is vital to improve strategic planning and management by governments and other stakeholders. The lack of such a framework is due to the fact that quantum computing per se is a highly technical domain, hence most of the existing studies focus heavily on the technical aspects of quantum computing. In contrast, our study highlights its practical and social uses cases, which are needed for the increased interest of governments. More specifically, our study took this call and offered a preliminary version of a framework for understanding the social, economic and political use cases of quantum computing, as well as identified possible areas of market disruption and offer empirically based recommendations that are critical for forecasting, planning, and strategically positioning QCs for accelerated diffusion, incl. definition of 52 Research Questions that will be critical for the adoption of quantum computing.


To this end, we conducted a gray literature research, whose outputs were structured in accordance with Dwivedi et al. (2021) that embodies environment, users, & application areas. We then validated through the discussing the findings with the quantum computing community at QWorld Quantum Science Days 2023 (QSD 2023) (on which I posted before 👉 here).

In short:

  • the hottest application areas are 🔥🔥🔥 business & finance, renewable energy, medicine & pharmaceuticals, & manufacturing 🔥🔥🔥;
  • at the level of environment – ecosystem, security, jurisprudence, institutional change & geopolitics;
  • users – customers, firms, countries or governments, to be more precise, with the reference to both national and local governments.

We then dived into these areas, and come up with the most popular & promising & overlooked topics, and as the very end-result, define 52 research questions, i.e., very specific things that are expected to be covered in the future to understand the current state-of-the-art, as well as transformations needed at various levels. The insights offered by various contributors from diverse disciplines – business, information systems, quantum computing, political science, and law offer a broad-based view of the potential of quantum computing to different aspects of our technological, economic, and social development. This framework is intended to help in identifying possible areas of market disruption offering empirically based recommendations that are critical for forecasting, planning, and strategically positioning prior to quantum computing emergence.

This is a truly a “happy end!” for the consortia that we built ~3 years ago – with Germany, Spain, Finland, Romania, and Latvia – while working on a project proposal to CHANSE call “Transformations: Social and Cultural Dynamics in the Digital Age”. We went there much far beyond my expectations, i.e. in fact, we were notified that this time we will not be granted the funding for the project at the very last stage, having gone through all those intermediate evaluation rounds, which were already fascinating news (at least for me). While working on the proposal and building our network, we conducted a preliminary analysis of the area, which then, regardless of the output of the application, we decided to continue and bring to at least some logical end. We like our result so decided to make it publicly available.

All in all, this is our warm welcome to read the paper -> here

And just in case you prefer a condensed version, you can just watch the video of the talk I delivered at QWorld Quantum Science Days 2023 (QSD 2023) 👇

References:

Dandison Ukpabi, Heikki Karjaluoto, Astrid Bötticher, Anastasija Nikiforova, Dragoş PETRESCU, Paulina Schindler, Visvaldis Valtenbergs, Lennard Lehmann, Framework for Understanding Quantum Computing Use Cases From A Multidisciplinary Perspective and Future Research Directions, Futures, 2023, 103277, ISSN 0016-3287, https://doi.org/10.1016/j.futures.2023.103277.

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., … & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.

📢New paper alert 📢“Predictive Analytics intelligent decision-making framework and testing it through sentiment analysis on Twitter data” or what people do and will think about ChatGPT?

Anastasija Nikiforova Leave a comment

This paper alert is dedicated to “Predictive Analytics intelligent decision-making framework and testing it through sentiment analysis on Twitter data” (authors: Otmane Azeroual, Radka Nacheva, Anastasija Nikiforova, Uta Störl, Amel Fraisse) paper, which is now publicly available in ACM Digital Library!

In this paper we present a predictive analytics-driven decision framework based on machine learning and data mining methods and techniques. We then demonstrate it in action by predicting sentiments and emotions in social media posts as a use-case choosing perhaps the trendiest topic – ChatGPT. In other words we check whether it is eternal love and complete trust or rather 🤬?

Why PA?

Predictive Analytics are seen to be useful in business, medical/ healthcare domain, incl. but not limited to crisis management, where, in addition to health-related crises, Predictive Analytics have proven useful in natural disasters management, industrial use-cases, such as energy to forecast supply and demand, predict the impact of equipment costs, downtimes / outages etc., aerospace to predict the impact of specific maintenance operations on aircraft reliability, fuel use, and uptime, while the biggest airlines – to predict travel patterns, setting ticket prices and flight schedules as well as predict the impact of, e.g., price changes, policy changes, and cancellations. And, of course, business process management and specifically retail, where Predictive Analytics allows retailers to follow customers in real-time, delivering targeted marketing and incentives, forecast inventory requirements, and configure their website (or store) to increase sales. It business process management area, in turn, Predictive Analytics give rise to what is called predictive process monitoring (PPM). Predictive Analytics uses were also found in Smart Cities and Smart Transportation domain, i.e. to support smart transportation services using open data, but also in education, i.e., to predict performance in MOOCs.

This popularity can be easily explained by examining their key strategic objectives, which IBM (Siegel, 2015) has summarized as: (1) competition – to secure the most powerful and unique stronghold of competitiveness, (2) growth – to increase sales and keep customers competitively, (3) enforcement – to maintain business integrity by managing fraud, (4) improvement – to advance core business capacity competitively, (5) satisfaction – to meet rising consumer expectations, (6) learning – to employ today’s most advanced analytics, (7) acting – to render business intelligence and analytics truly effective actionable. Marketing, sales, fraud detection, call center and core businesses of business units, same as customers and the enterprise as  a whole are expected to gain benefits, which makes PA a “must”.

And although according to (MicroStrategy, 2020), in 2020, 52% of companies worldwide used predictive analytics to optimize operations as part of business intelligence platform solution, although so far, predictive analytics have been used mostly by large companies (65% of companies with $100 million to $500 million in revenue, and 46% of companies under $10 million in revenue), with less adoption in medium-sized companies, not to say about small companies. 

Based on management theory and Gartner’s Business Intelligence and Performance Management Maturity Model, our framework covers four management levels of business intelligence – (a) Operational, (b) Tactical, (c) Strategic and (d) Pervasive. These are the levels that determine the need to manage data in organizations, transform them into information and turn them into knowledge, which is also the basis for making forecasts. The end result of applying it for business purposes is to generate effective solutions for each of these levels.

Sounds catchy? Read the paper here.

Many thanks to my co-authors – Radka and Otmane, who invited me to contribute to this study, and drove the entire process!

Cite paper as:

O. Azeroual, R. Nacheva, A. Nikiforova, U. Störl, and A. Fraisse. 2023. Predictive Analytics intelligent decision-making framework and testing it through sentiment analysis on Twitter data. In Proceedings of the 24th International Conference on Computer Systems and Technologies (CompSysTech ’23). Association for Computing Machinery, New York, NY, USA, 42–53. https://doi.org/10.1145/3606305.3606309

📢🚨⚠️Paper alert! Overlooked aspects of data governance: workflow framework for enterprise data deduplication

Anastasija Nikiforova Leave a comment

This time I would like to recommend for reading the new paper “Overlooked aspects of data governance: workflow framework for enterprise data deduplication” that has been just presented at the IEEE-sponsored International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS2023). This “just”, btw, means June 19 – the day after my birthday, i.e. so I decided to start my new year with one more conference and paper & yes, this means that again, as many of those who congratulated me were wishing – to find the time for myself, reach work-life balance etc., is still something I have to try to achieve, but this time, I decided to give a preference to the career over my personal life (what a surprise, isn’t it?) 🙂 Moreover, this is the conference, where I am also considered to be part of Steering committee, Technical Program committee, as well as publicity chair. During the conference, I also acted as a session chair of its first session, what I consider to be a special honor – for me the session was very smooth, interactive and insightful, of course, beforehand its participants & authors and their studies, which allowed us to establish this fruitful discussion and get some insights for our further studies (yes, I also got one beforehand one very useful idea for further investigation). Thank you all contributors, with special thanks to Francisco Bonilla Rivas, Bruck Wubete, Reem Nassar, Haitham Al Ajmi.

And I am also proud with getting one of four keynotes for this conference – prof. Eirini Ntoutsi from the Bundeswehr University Munich (UniBw-M), Germany, who delivered a keynote “Bias and Discrimination in AI Systems: From Single-Identity Dimensions to Multi-Discrimination“, which I heard during one of previous conferences I attended and decided that it is “must” for our conference as well – super glad that Eirini accepted our invitation! Here, I will immediately mention that other keynotes were excellent as well – Giancarlo Fortino (University of Calabria, Italy), Dofe Jaya (Computer Engineering Department, California State University, Fullerton, California, USA), Sandra Sendra (Polytechnic University of Valencia, Spain).

The paper I presented is authored in a team of three – Otmane Azeroual, German Centre for Higher Education Research and Science Studies (DZHW), Germany, myself – Anastasija Nikiforova, Faculty of Science and Technology, Institute of Computer Science, University of Tartu, Estonia & Task Force “FAIR Metrics and Data Quality”, European Open Science Cloud & Kewei Sha, College of Science and Engineering University of Houston Clear Lake, USA – very international team. So, what is the paper about? It is (or should be) clear that data quality in companies is decisive and critical to the benefits their products and services can provide. However, in heterogeneous IT infrastructures where, e.g., different applications for Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), product management, manufacturing, and marketing are used, duplicates, e.g., multiple entries for the same customer or product in a database or information system, occur. There can be several reasons for this (incl. but not limited due to the growing volume of data, incl. due to the adoption of cloud technologies, use of multiple different sources, the proliferation of connected personal and work devices in homes, stores, offices and supply chains), but the result of non-unique or duplicate records is a degraded data quality, which, in turn, ultimately leads to inaccurate analysis, poor, distorted or skewed decisions, distorted insights provided by Business Intelligence (BI) or machine learning (ML) algorithms, models, forecasts, and simulations, where the data form the input, and other data-driven activities such as service personalisation in terms of both their accuracy, trustworthiness and reliability, user acceptance / adoption and satisfaction, customer service, risk management, crisis management, as well as resource management (time, human, and fiscal), not to say about wasted resources, and employees, who are less likely trust the data and associated applications thereby affecting the company image. This, in turn, can lead to a failure of a project if not a business. At the same time, the amount of data that companies collect is growing exponentially, i.e., the volume of data is constantly increasing, making it difficult to effectively manage them. Thus, both ex-ante and ex-post deduplication mechanisms are critical in this context to ensure sufficient data quality and are usually integrated into a broader data governance approach. In this paper, we develop such a conceptual data governance framework for effective and efficient management of duplicate data, and improvement of data accuracy and consistency in medium to large data ecosystems. We present methods and recommendations for companies to deal with duplicate data in a meaningful way, while the presented framework is integrated into one of the most popular data quality tools – Data Cleaner.

In short, in this paper we:

  • first, present methods for how companies can deal meaningfully with duplicate data. Initially, we focus on data profiling using several analysis methods applicable to different types of datasets, incl. analysis of different types of errors, structuring, harmonizing, & merging of duplicate data;
  • second, we propose methods for reducing the number of comparisons and matching attribute values based on similarity (in medium to large databases). The focus is on easy integration and duplicate detection configuration so that the solution can be easily adapted to different users in companies without domain knowledge. These methods are domain-independent and can be transferred to other application contexts to evaluate the quality, structure, and content of duplicate / repetitive data;
  • finally, we integrate the chosen methods into the framework of Hildebrandt et al. [ref 2]. We also explore some of the most common data quality tools in practice, into which we integrate this framework.

After that, we test and validate the framework. The final refined solution provides the basis for subsequent use. It consists of detecting and visualizing duplicates, presenting the identified redundancies to the user in a user-friendly manner to enable and facilitate their further elimination.

With this paper we aim to support research in data management and data governance by identifying duplicate data at the enterprise level and meeting today’s demands for increased connectivity / interconnectedness, data ubiquity, and multi-data sourcing. In addition, the proposed conceptual data governance framework aims to provide an overview of data quality, accuracy and consistency to help practitioners approach data governance in a structured manner.

In general, not only technological solutions are needed that would identify / detect poor quality data and allow their examination and correction, or would ensure their prevention by integrating some controls into the system design, striving for “data quality by design” [ref3, ref4], but also cultural changes related to data management and governance within the organization. These two perspectives form the basis of the wealth business data ecosystem. Thus, the presented framework describes the hierarchy of people who are allowed to view and share data, rules for data collection, data privacy, data security standards, and channels through which data can be collected. Ultimately, this framework will help users be more consistent in data collection and data quality for reliable and accurate results of data-driven actions and activities.

Sounds interesting? Read the paper -> here (to be cited as: Azeroual, O., Nikiforova, A., Sha, K. (2023, June). Overlooked aspects of data governance: workflow framework for enterprise data deduplication. In 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS2023). IEEE (in print))

International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS2023) is collocated with The International Conference on Multimedia Computing, Networking and Applications (MCNA2023), which are sponsored by IEEE (IEEE Espana Seccion), Universitat Politecnica de Valencia, Al ain University. Great thanks to the organizers – Jaime Lloret, Universitat Politècnica de València, Spain & Yaser Jararweh, Jordan University of Science and Technology, Jordan & Marios C. Angelides, Brunel University London, UK & Muhannad Quwaider, Jordan University of Science and Technology, Jordan.

References:

Azeroual, O., Nikiforova, A., Sha, K. (2023, June). Overlooked aspects of data governance: workflow framework for enterprise data deduplication. In 2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS2023). IEEE (in print).

Hildebrandt, K., Panse, F., Wilcke, N., & Ritter, N. (2017). Large-scale data pollution with Apache Spark. IEEE Transactions on Big Data, 6(2), 396-411

Guerra-García, C., Nikiforova, A., Jiménez, S., Perez-Gonzalez, H. G., Ramírez-Torres, M., & Ontañon-García, L. (2023). ISO/IEC 25012-based methodology for managing data quality requirements in the development of information systems: Towards Data Quality by Design. Data & Knowledge Engineering, 145, 102152.

Corrales, D. C., Ledezma, A., & Corrales, J. C. (2016). A systematic review of data quality issues in knowledge discovery tasks. Revista Ingenierías Universidad de Medellín, 15(28), 125-150.

💬💬💬 Contributed talk for QWorld Quantum Science Days 2023 (QSD 2023)

Anastasija Nikiforova 1 Comment

In the very last days of May 2023, I had yet another experience – I delivered a contributed talk at QWorld Quantum Science Days 2023 (QSD 2023) titled “Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions” (Ukpabi, D.C., Karjaluoto, H., Botticher, A., Nikiforova, A., Petrescu, D.I., Schindler, P., Valtenbergs, V., Lehmann, L., & Yakaryılmaz, A), which, in fact, is based on the paper we made publicly available some time ago and developed it even earlier when together with Germany, Spain, Finland, Romania, and Latvia we built a consortia and submitted a project proposal to CHANSE call “Transformations: Social and Cultural Dynamics in the Digital Age”. We went there much far beyond my expectations, i.e. in fact, we were notified that this time we will not be granted the funding for the project at the very last stage, having gone through all those intermediate evaluation rounds, which were already fascinating news (at least for me). While working on the proposal and building our network, we conducted a preliminary analysis of the area, which then, regardless of the output of the application, we decided to continue and bring to at least some logical end. We like our result so decided to make it publicly available. And now, a few years from that, we submitted our work to QWorld Quantum Science Days 2023 (QSD 2023) and were accepted. It was a big surprise, and I, as the person delegated by our team to present our study, delivered this talk, where I finally familiarized the audience with our findings. What was my surprise when my talk, which followed immediately after the keynote “Let’s talk about Quantum; Societal readiness through science communication research” delivered on behalf of Quantum DELTA NL by Julia Cramer, was in the very similar direction? It is also worth mentioning a very interesting coincidence that while the keynote elaborated on the DELTA that stands for five major quantum hubs, namely Delft, Eindhoven, Leiden, Twente, Amsterdam, I was preparing the last things for my presentation located in the Delta building – it is the name of the building my office is located in. In both cases, no connection with COVID-19 😀

🤔 What is the paper about?

There has been increasing awareness of the tremendous opportunities inherent in quantum computing. It is expected that the speed and efficiency of quantum computing will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased quantum computing research funding from national and regional governments and private firms. However, ❗❗❗ critical concerns regarding legal, political, and business-related policies germane to quantum computing adoption exist ❗❗❗

Since this is an emerging and highly technical domain, most of the existing studies focus heavily on the technical aspects of quantum computing. In contrast, our study highlights its practical and social uses cases, which are needed for the increased interest of governments. More specifically, our study offers a multidisciplinary review of quantum computing, drawing on the expertise of scholars from a wide range of disciplines whose insights coalesce into a framework that simplifies the understanding of quantum computing, identifies possible areas of market disruption and offer empirically based recommendations that are critical for forecasting, planning, and strategically positioning QCs for accelerated diffusion.

"Framework for understanding quantum computing use cases from a multidisciplinary perspective and future research directions" (Ukpabi, D.C., Karjaluoto, H., Botticher, A., Nikiforova, A., Petrescu, D.I., Schindler, P., Valtenbergs, V., Lehmann, L., & Yakaryılmaz, A)

To this end, we conducted a gray literature research, whose outputs were then structured in accordance with Dwivedi et al., 2021 (Dwivedi et al. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168), which embodies three broad areas—environment, users, and application areas—and the dominant sub-themes presented in figure below. We found that for application areas, business and finance, renewable energy, medicine & pharmaceuticals, and manufacturing are now the hottest. While for environment, we found subdomains such as ecosystem, security, jurisprudence, institutional change & geopolitics. And for the users, nothing surprising – as typically, customers, firms, countries. We then dive into each of those areas, as well as later come up with the most popular topics, the most promising, and overlooked.

Sounds interesting? Read the paper here, find slides here, watch video here.

Quantum Science Days is an annual, international, and virtual scientific conference organized by QWorld (Association) to provide opportunities to the quantum community to present and discuss their research results at all levels (from short projects to thesis work to research publications), and to get to know each other. The third edition (QSD2023) included 7 invited speakers, 10 thematic talks on “Building an Open Quantum Ecosystem”, 31 contributed talks, an industrial demo session by Classiq, and a career talk on quantum. QSD2023 was sponsored by Unitary Fund & Classiq and supported by Latvian Quantum Initiative.

Qworld