Related papers: Characterizing Automated Data Insights
Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing…
The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection. However, key areas such as utilizing domain knowledge and data semantics are…
Recommender systems (RSs) have become an essential tool for mitigating information overload in a range of real-world applications. Recent trends in RSs have revealed a major paradigm shift, moving the spotlight from model-centric…
Sports analytics -- broadly defined as the pursuit of improvement in athletic performance through the analysis of data -- has expanded its footprint both in the professional sports industry and in academia over the past 30 years. In this…
Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution…
Data visualization should be accessible for all analysts with data, not just the few with technical expertise. Visualization recommender systems aim to lower the barrier to exploring basic visualizations by automatically generating results…
There is a growing interest in designing tools to support interactivity specification and authoring in data visualization. To develop expressive and flexible tools, we need theories and models that describe the task space of interaction…
Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large…
Context. Advancements in Machine Learning (ML) are revolutionizing every application domain, driving unprecedented transformations and fostering innovation. However, despite these advances, several organizations are experiencing friction in…
Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are…
Everyone eats. However, people do not always know what to eat. They need a little help and inspiration. Consequently, a number of apps, services, and programs have developed recommenders around food. These cover food, meal, recipe, and…
We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language…
The selection of datasets in recommender systems research lacks a systematic methodology. Researchers often select datasets based on popularity rather than empirical suitability. We developed the APS Explorer, a web application that…
The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, among others, has grown rapidly over the past decade. With this…
Labeled data sets are necessary to train and evaluate anomaly-based network intrusion detection systems. This work provides a focused literature survey of data sets for network-based intrusion detection and describes the underlying packet-…
Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided e.g. in consultancy via the use of recommender…
The visual analysis of retinal data contributes to the understanding of a wide range of eye diseases. For the evaluation of cross-sectional studies, ophthalmologists rely on workflows and toolsets established in their work environment. That…
Humans are expert in the amount of sensory data they deal with each moment. Human brain not only analyses these data but also starts synthesizing new information from the existing data. The current age Big-data systems are needed not just…
Artificial intelligence is reshaping science and industry, yet many users still regard its models as opaque "black boxes". Conventional explainable artificial-intelligence methods clarify individual predictions but overlook the upstream…
Optimization is ubiquitous in our daily lives. In the past, (sub-)optimal solutions to any problem have been derived by trial and error, sheer luck, or the expertise of knowledgeable individuals. In our contemporary age, there thankfully…