As they have been for decades, databases are a key component in various types of business applications. Nowadays, modern databases also include features that support the construction of applications based on artificial intelligence. The starting point of this paper is the questions of what are the typical use cases in which a database and generative artificial intelligence work together, what database features are used in these applications, and what other technologies the applications rely on. The results contribute to one of our practice-oriented research projects, in which we build an environment that supports AI experiments for participating companies. As input, we use open online learning materials provided by three database vendors. The databases we selected for review are all non-relational, representing three different genres, and are the most popular representatives of their genre. For each training program of the vendors, we will examine the structure and scope of the program, the courses on generative AI included in the program, and the use cases and technologies related to generative AI presented in them. Finally, we will prepare a summary of the use cases and technologies found.

The article focuses on one aspect of artificial intelligence – Generative AI (GenAI) – which is expected to offer significant opportunities in different areas of business, industry, and society. GenAI is the current state of the decades-long development of artificial intelligence (AI), and many companies are currently looking for ways to benefit from this market-changing technology. The use of GenAI in business practices is topical among companies and organizations, and decision-makers across the globe are considering the future potential of GenAI and large language models (LLM) for organizations and businesses. The aim of this paper is to examine the utilization of artificial intelligence in business operations and industry, emphasizing both the opportunities offered by GenAI as well as the challenges related to its usage. Additionally, the paper strives to determine whether the phenomenon is real or merely hype, as well as addressing its so-called revolutionary status. The topic is approached through a light literature review and discussion on the findings of two studies carried out in Finnish companies related to GenAI utilization.

In this article, the ongoing research on collaborative prototype development between university and enterprises is presented. The study of project featured numerous pilot cases and prototypes, executed in collaboration with organizations to address real-world challenges. This article assesses the appropriateness of the Descriptive Model for Prototyping Process (DMPP) for research project applications. We delve into two primary facets: the synergy between universities and enterprises, and the potential for artifact reusability within the DMPP. The article presents various pilot cases from the KIEMI project, highlighting the DMPP’s role in each. Furthermore, the paper evaluates the model, sets forward the challenges faced, and, finally, discusses topics for future research.

The paper examines the Third Mission of universities from the point of view of company collaboration in the prototype development process. The paper presents an implementation of university-enterprise collaboration in prototype development described by means of process modeling notation. In this article, the focus is on modeling the software prototyping process in a research context. This research paper introduces prototype development in a university environment. The prototypes are made in collaboration with companies, which offered real-world use cases. The prototype development process is introduced by a modeling procedure with four example prototype cases. The research method used is an eight-step process modeling approach. The goal was to find instances of activity, artifact, resource, and role. The results of modeling are presented using textual and graphical notation. This paper describes the data elicitation, where the process knowledge is collected using stickers-on-the-wall technique, and the creation of the model is described. Finally, the shortcomings found in our existing practices and possibilities for improving our prototype development processes and practices are discussed.

In this article the focus is on software evolution, which is an important part of software engineering. In practice, software development does not stop when a system is delivered but continues throughout the lifetime of the system. After the system has been deployed, external pressure for change can generate new requirements for the existing software. This change aspect, which is a characteristic of software engineering, should be taken into consideration when developing and modeling new software systems. In this paper the theme was studied using experience gained from the piloting of a reference system developed in an earlier research project carried out by T ampere University of Technology. Software evaluation is examined from the point of view of system developers, administrators (maintenance), and end users based on a concrete long-term piloting period.

With the rapid growth of image and video data as well as the fast spread of user-generated content in social media and cloud services, it has become increasingly difficult for users to have efficient access and effective management of their digital content. In this paper we present a novel integrated open source multimedia content management and access framework, called VisualLabel, that enables smart photo services based on automated visual content analysis, annotation, search and retrieval using state of the art analysis back ends for services such as Facebook and Flickr. This paper includes detailed descriptions of the high-level architecture used in the VisualLabel framework and proof-of-concept implementations of a front-end service, along with three analysis back ends and a web client, all of which demonstrate the basic functionality provided by the framework.

Enormous quantities of data, collected and stored in large numerous data repositories, go unused or underused today, simply because people are unable to visualize the quantities and relationships involved. This huge amount of data has far exceeded our human ability for comprehension without powerful tools. Information visualization and visual data mining can help to deal with the flood of information. We can take advantage of visualization techniques to discover data relationships that are otherwise not easily observable by looking at the “raw data”. Visualization can add significant value when trying to understand not only the raw data available in large software archives, but also the results of data mining. These data are valuable especially in software maintenance activities, understanding software evolution and the socio-technical aspects of software development. Data mining and visualization are focal enablers for information recognition and knowledge discovery from any amount of data repositories. This paper present the results of a survey, which reviews some of the most common visual data mining (VDM) techniques and their usage in the software engineering field. The results indicate what kinds of aspects of the software engineering process are studied using VDM methods, and also the most common VDM methods used in the software engineering context.

A clean environment is often taken for granted, but when a river or a lake becomes polluted, it might be hard for the general public to verify the condition. A simple visualization tool for checking the condition of water could help to inform the public and help to increase environmental awareness. With the emergence of Open Data and other openly available data sources, it is possible to create new and innovative applications. In this paper we present a web-based tool for reviewing the quality of water by applying the River Heavy Metal Evaluation Index (rHMEI) method together with open data on water quality in Finland.

Attaching any kind of clue – event, location, person, tag or keyword – to a photo eases the process of searching. Often the problem is that the user feels that it is difficult to think of good tags or that the tagging process is too tedious or cumbersome. At the same time, users use social media daily, and write about topics they feel are important and that they are actively interested in. This paper presents a method for extracting metadata (tag suggestions) from social media profiles and illustrates the use of the tags for photo tagging by means of a web-based photo application.