Unstructured Format Research Articles

Innovative of Area-Based Learning to Transfer Community Innovation through Phulae Pineapple Living Encyclopedia in Chiang Rai, Thailand

The objectives of the research were 1) to study community innovation knowledge through the Phulae pineapple living encyclopedia in Chiang Rai, Thailand, 2) to develop innovative area-based learning to transfer community innovation through the Phulae pineapple living encyclopedia and to study the results of using innovative of area-based learning through Phulae pineapple living encyclopedia. The methodology of the research was based on Participatory Action Research (PAR). The sample group consisted of 20 community innovation informants and a group of 405 people assessing satisfaction with the Phulae pineapple living encyclopedia accounting for a total of 425 people. The instrument used consisted of 1) unstructured interview forms, 2) forms of encyclopedia quality assessment, and forms of satisfaction assessment. Qualitative data was analyzed based on content analysis and the presentation was in the form of descriptive analysis. Quantitative data was analyzed based on a primary statistical package to find the mean and standard deviation. Study results revealed that: 1. The study on community innovation knowledge through Phulae pineapple living encyclopedia in Chiang Rai, Thailand revealed that Phulae pineapple community innovation knowledge is divided into 5 issues, namely 1) Phulae: Sustainable culture of happiness, 2) Phulae: Self-reliant agriculture community, 3) Phulae: Creative local economy, 4) Phulae: History for living with balance and 5) Phulae: Topography for agricultural tourism; 2. Developing innovative area-based learning to transfer community innovation through Phulae pineapple living encyclopedia revealed that innovative area-based learning developed to be Phulae pineapple living encyclopedia consists of 4 parts, namely 1) BEGIN until becoming ... Phulae, 2) BEHIND Phulae ...background of happiness, 3) TO BE CONTINUE Phulae with sustainable security and 4) Conclusion and the application named Phulae has been developed to be used together with the developed encyclopedia. The results of the assessment conducted by experts revealed that the quality of the Phulae pineapple living encyclopedia was at a very high level and it can be disseminated; 3. The study on using innovative area-based learning to transfer community innovation through the Phulae pineapple living encyclopedia revealed that the group of people assessing satisfaction over the Phulae pineapple living encyclopedia assessed the satisfaction to be at a high level in an overall picture. Moreover, it was found that among the group cultivating and distributing Phulae pineapple, incomes in the household have increased from accessing the goods of the consumer group receiving information of disseminating knowledge through the living encyclopedia and the developed application.

Assessing the Contribution of Scanned Outside Documents to the Completeness of Real-World Data Abstraction.

Electronic health record (EHR) data are widely used in precision medicine, quality improvement, disease surveillance, and population health management. However, a significant amount of EHR data are stored in unstructured formats including scanned documents external to the treatment facility presenting an informatics challenge for secondary use. Studies are needed to characterize the clinical information uniquely available in scanned outside documents (SODs) to understand to what extent the availability of such information affects the use of these real-world data for cancer research. Two independent EHR data abstractions capturing 30 variables commonly used in oncology research were conducted for 125 patients treated for advanced non-small-cell lung cancer at a comprehensive cancer center, with and without consideration of SODs. Completeness and concordance were compared between the two abstractions, overall, and by patient groups and variable types. The overall completeness of the data with SODs was 77.6% as compared with 54.3% for the abstraction without SODs. The differences in completeness were driven by data related to biomarker tests, which were more likely to be uniquely available in SODs. Such data were prone to missingness among patients who were diagnosed externally. There were no major differences in completeness between the two abstractions by demographics, diagnosis, disease progression, performance status, or oral therapy use. However, biomarker data were more likely to be uniquely contained in the SODs. Our findings may help cancer centers prioritize the types of SOD data being abstracted for research or other secondary purposes.

A Brief Survey of Text Document Classification Algorithms and Processes

The exponential growth of unstructured data is one of the most critical challenges in data mining, text analytics, or data analytics. Around 80% of the world's data are available in unstructured format and most are left unattended due to the complexity of its analysis. It is a great challenge to guarantee the quality of the text document classifier that classifies documents based on user preferences because of large-scale terms and data patterns. The World Wide Web is growing rapidly and the availability of electronic documents is also increasing. Therefore, the automatic categorization of documents is the key factor for the systematic organization of information and knowledge discovery. Most existing widespread text mining and classification strategies have adopted term-based approaches. However, the problems of polysemy and synonymy in such approaches are of great concern. To classify documents based on their context, the context-based approach is needed to be followed. Semantic analysis of the text overcomes the limitations of the term-based approach and it also enhances the accuracy of the classifiers. This paper aims to highlight the important algorithms, techniques, and methodologies that can be used for text document classification. Furthermore, the paper also provides a review of the different stages of Text Document Classification.

Comparative Analysis of ETL Tools in Big Data Analytics

Massive amounts of data are being generated these days all around the world. The majority of social networking platforms, ecommerce sites, the public and private sectors, healthcare institutions, cloud networks, and various servers are all generating immense amount of data. Collected data from various sites could be in a structured or unstructured format. Extract Transform Load (ETL) is a crucial component of the growing demand for quicker business decisions aimed at many contemporary applications. Due to the volume and speed of data, real-time ETL is built on the foundation of multi-source, unstructured data stream extraction and transformation employing disc data in dispersed environments. The entire procedure is pipelined while processing so that the final resultant data can provide some essential and useful conclusions to work on. Some analytical findings are once again helpful in making decisions. However, the produced results may differ in some numbers, graphs, and figures in many circumstances. This occurs as a result of the usage of some unrealistic tools for big data processing. This paper proposed several methods and appropriate ETL (extract, transform, and load) tools for the big data processing, which may result in appropriate analytics and conclusions from the data.

Hybrid Recurrent Neural Network Modeling for Traffic Delay Prediction at Signalized Intersections Along an Urban Arterial

This paper studies the traffic delay prediction modeling for multiple signalized intersections along the Ala Moana Boulevard and Nimitz Highway in Hawaii. Several machine learning (ML) based approaches have been studied in the literature, and most of them focused on prediction accuracy rather than the end use of real-time control and implementation. These ML models tend to be very complex and non-linear in nature, making it challenging to achieve fast inferences and are computationally heavy for real-time signal control implementation. In this paper, a simple yet accurate hybrid modeling method is proposed to predict traffic delay one-step ahead with the model made suitable for real-time implementation to control traffic flow. Since real-time road-side measurements are recorded in unstructured form, the paper also discusses other issues related to data extraction and the pre-processing process. Finally, a simple signal control loop is developed to demonstrate the proposed modeling approach, which has shown advantages in model accuracy and computation efficiency compared against several existing modeling methods.

УПРАВЛІННЯ НЕСТРУКТУРОВАНИМИ ДАНИМИ ПРИ ДОСЛІДЖЕННІ ВПЛИВУ ЗАСОБІВ МАСОВОЇ ІНФОРМАЦІЇ НА СОЦІАЛЬНУ БЕЗПЕКУ ЛЮДИНИ В УМОВАХ ГІБРИДНОЇ ВІЙНИ

Russia’s war against Ukraine significantly affects the state of Ukraine's national security and the social security of every Ukrainian. Its negative consequences are both direct and indirect in nature, which is a real social threat. The degree (level) of protection of the vital socio-economic interests of a person (person), his rights, freedoms and values from internal and external, real, and potential threats is an important element of assessing the state of the state's national security. In the conditions when Russia's military and informational aggression is directed against Ukraine, the study of issues of media influence, both from the inside and from the outside, on public opinion, moods among different categories of citizens, ethnic communities, their manifestations, and expression of will regarding political and economic transformations in the country is important and relevant. An important element of the analysis of the state of human social security in the conditions of a hybrid war is the study and assessment of the structure and dynamics of the development of public opinion in certain regions of the country. One of the most serious ways of assessing public opinion and forecasting its changes is, along with sociological research, monitoring the influence of mass media on the population of the region. Traditionally, such influence is assessed by monitoring local mass media and evaluating them according to a few formal qualities, such as audience coverage, ownership structure, political connections of owners, signs of commitment to one or another political position, etc. Such an analysis allows with a certain degree of accuracy to record the presence of various centers of influence on public opinion, however, it is completely insufficient to assess the real influence of these centers on public opinion itself, since it does not allow to assess exactly how this or that information influenced its consumers. When processing information flows from mass media, social networks, e-mail, etc., there are constant problems of processing large volumes of data containing textual information, which, although related to location, is not presented in traditional structured spatial formats, and does not explicitly contain geographic coordinates. Instead, its connection to geography is hidden in unstructured or semi-structured formats based on plain text. Unstructured data is usually considered to be information that does not have a predefined data structure or is not organized in a fixed order. Unstructured data, as a rule, is presented in the form of text, which can contain data such as dates, numbers, and facts, which immediately leads to difficulties in analysis, especially in the case of using traditional programs designed to work with structured data. Unstructured data is usually stored in human-readable formats, although such formats make automatic data management difficult. In connection with the rapid pace of progress and development of geoinformation systems, the specialists of the company Esri (USA) comprehensively approached the problem of the combined use of unstructured data and geoinformation systems. The Locate XT module was created to solve this problem. The new ArcGIS LocateXT add-on is designed to search, extract, and map data from plain text, including geographic coordinates in various formats, place names, and other location-related information. Geoinformation technology LocateXT is an effective tool for research and analysis of the impact of mass media in the conditions of a hybrid war between Russia and Ukraine on human social security, efficiency, completeness and quality of information and spatial support of management processes. It helps speed up decision-making procedures based on quality management of unstructured data obtained from disparate sources. Modern geoinformation tools in the decision-making support system provide an opportunity to systematize unstructured data, determine their geospatial reference, and subsequently conduct analysis and form the obtained results in a form convenient for perception.

A Centralized MongoDB-Based Repository Design for IIoT Data: The ecoKI Project*

One of the challenges of cyber-physical systems is the data acquisition in real- time through different communication protocols within the scope of the Industrial Internet of Things (IIoT). This IIoT data is mainly specified as big in amount, continuous in nature, unstructured, and ambiguous format. It is also enigmatic in terms of structure for an overview of the manufacturing plant and which data belongs to which sensor or machine. Presently, relational database technologies are inadequate to cater to such kinds of IIoT data due to their strict data structure and relations. For this reason, we presented the ontology-based data repository model implementation for the process industry using MongoDB, which is a cross- platform document-oriented, most sought-after ample data storage database system nowadays. Firstly, we have designed a JSON-based data-model schema for the IIoT data repository, which gathers the data with the help of one of the most commonly used communication protocols within the scope of IIoT, OPC UA. Secondly, our schema stores the meta-data and data using embedded referencing through the same data-model. Hence, the scalability, flexibility, and variety of the data storage system have increased. Lastly, with the execution of insertion and search queries, we show how fast to get the desired data element hierarchy or track any particular data, which becomes possible due to the proposed ontology. This effectiveness of the ontology-based data-model also minimize the overhead of the machine learning engineer job.

Surface modification and functionalization of meso-sized carbon tubes

In this article, the specific surface area (Ssol) and pore size distribution were determined by BET absorption at a low temperature of 77K. The purpose of the work is to determine the level of functionality and surface modification of meso-sized carbon tubes. According to the BET model, the specific surface area was calculated and the BJH model of the pore size distribution in the material was used. The morphology of mesoporous carbon was studied using transmission electron microscopy and scanning electron microscopy. Differential scanning calorimetry and thermogravimetric analysis of the synthesis products were performed in the air to determine the leaching conditions. According to thermogravimetric data, the amount of unstructured forms of meso-sized carbon in the samples of meso-sized carbon tubes with a multi-walled mesoporous structure decreases several times, from 2.0-8.0% to 0.5-2%. Electron microscopy confirmed the effectiveness of air annealing. A comparison of different methods of processing meso-sized carbon tubes with a multi-walled mesoporous structure shows the advantages of using nitric acid as a modifying agent, since it promotes the formation of structural fragments of the most molecules among the oxidizing agents used, which are easy to use and by simple washing with water is easily removed from the system without absorption in meso-sized carbon tubes with a multi-walled mesoporous structure. Meso-sized carbon tubes with a multi-walled mesoporous structure processed in nitric acid, as well as the first ones, showed stability up to 450-500 °C when heated in air. Their structural and electronic characteristics, calculated from Raman spectra and diffraction data, remained virtually unchanged. The method of X-ray diffraction of oxidized samples indicates almost complete removal of the metal.

Geological profile-text information association model of mineral exploration reports for fast analysis of geological content

Mineral exploration reports include not only a large number of geological profiles but also geological text by offering valuable information and knowledge about the geological environments in which mineral deposits form. Extracting and understanding historical data can assist in the fast analysis of geological content and support 3D model construction. However, geological texts are written in unstructured form and have many correlations with geological profiles. It is a challenging task to derive meaningful geological information without manually reading through a large collection of reports, which is a formidable task for geologists. This paper proposes a geological profile-text association framework for constructing a knowledge graph, and it aims to understand the contents of the geological profile, transform a larger amount of textual data into structure form, and link the geological profile and text to a graph-based knowledge representation that assists further analysis of knowledge discovery. The concept of constructing vector geological profile rock layer objectification is proposed to make each rock layer with geometric features and attribute information for the geological profile, and the geological entity relationship is extracted to form a triple by deep learning and stored and expressed in the form of a graph structure for geological text. Finally, a geological profile and text association model is established by word vector similarity. The proposed approach is capable of rapidly and robustly understanding geological profiles, extracting geological texts, establishing correlations between them, and performing geological knowledge mining.

Intelligent mining of safety hazard information from construction documents using semantic similarity and information entropy

Project construction on-site is known to be very dangerous workplace environments due to large numbers of safety hazards. Analysis of construction safety hazards is essential to formulate rational safety management plans and prevent accidents. Construction documents contain large volumes of safety hazard information available for analysis. However, such analyses are challenging because the safety hazard information in the construction documents is presented in an unstructured or semi-structured format. This study proposes a method for intelligent mining of safety hazard information, which comprises safety hazard technical term recognition and safety hazard information analysis. The safety hazard technical term recognition model is developed based on semantic similarity and information correlation to build a safety hazard technical term library. The safety hazard information based on the technical term library is mined and analyzed using the term frequency-inverse document frequency method (TF-IDF). Finally, the proposed method is applied to build the safety hazard technical term library, which contains 2697 technical terms, and develop a hydraulic project construction safety hazard analysis system, which can realize the intelligent recognition and application of technical terms. Meanwhile, this system can automatically extract safety hazard information and provide a visualization interface to intuitively show the safety hazard analysis results, which improves the extraction efficiency of safety hazard information. The study provides a new approach for recognizing technical terms and mining safety hazard information, which can lead to enhancing management efficiency and practical knowledge discovery for safety management.

Technology and Innovation in Global Ophthalmology: The Past, the Potential, and a Path Forward.

Technology and Innovation in Global Ophthalmology: The Past, the Potential, and a Path Forward.

Creation of Pari Tanyong

The objective of this creative research entitled “Creation of Pari Tanyong” was to compose Pari Tanyong using qualitative research methodology conducted by studying about the concepts, theories, academic subjects, related research, concepts and theories in Thai music, concepts and theories of Thai music composition, concepts and theories concept of aesthetics, concepts and theories of art creation, academic subjects about Tanyong songs including research related to Thai music composition in order to define the scope and process of composition. There were two types of tools in this research: unstructured interview forms and work assessment forms. Theresults were presented by using a traditional southern folk song (Rong-Ngeng).The researcher composed the melody using the amplified composition and creating an augmentation to 8 bars by dropping the melody to 8th bar which had the same sound at the end of the original melody. The newly composed melody was discussed by descriptive analysis. The results from the research process of creation of Pari Tanyong were
 1) consisted of melodies of 6 songs as follows: Cheerful song (Lagu-Dua song): an instrumental song consisting of 3-verse melodies and 4 sentences per verse.
 2) Joyous song (Soi Rakham): lyrics consisted of 1-verse melody and 6 sentences per verse.
 3) Yahong song: the researcher used the original melody in order to conform the tradition of Tanyong
 song consisting of 1-verse melody, 2 sentences per verse, and 2 bars.
 4) Relaxing song (Jingli Nona): an instrumental song consisting of the main melody and the ending melody, totaling 8 sentences.
 However, when assessing the suitability of the melody by 5 experts, it was found that there was a highly suitable level.
 
 Keywords: Creation, Southern folk song, Pari Tanyong, Tanyong songs, Thai music composition

ANALISIS TRENDING TOPIC PADA BLOG SEPEDA MOTOR DI INDONESIA DENGAN MENGGUNAKAN TEXT PROCESSING STUDI KASUS IWANBANARAN.COM

This research was conducted because of the rapid flow of information that takes place along with the development of information and technology. Information technology was developed in order to facilitate and improve human performance in daily life. One form of data that is widely used by us is text data. Text data is more difficult to process because of its unstructured form. Text processing is a part of data mining which consists of the process of extracting data in a very large number to retrieve important information and interesting patterns in it. In this study, text processing is done to obtain data on the patterns and the trends of data that often appears in a web. Data source obtained by the author from direct observation in the web page. Data taken consists of article title, posting date, tags, and link each article. The data collected together for further reprocessed using text processing and displayed through the Graphical User Interface display, better known by the term GUI for easily understood data by users. The result from this study is the tabular report that describes the trend of the discussion of each month from January to June, the trending topics for six months based on the number of documents and the total appearance, as well as the graphs showing the development trend of topics over time. Keywords: Analysis, Measurement, Blog, Tags, Text Processing, Tokenizing, Filtering, Database, RapidMiner, Graphical User Interface.

A Novel Threat Intelligence Information Extraction System Combining Multiple Models

The increasing number of cyberattacks has made the cybersecurity situation more serious. Thus, it is urgent to use cyber threat intelligence to deal with the complex and changing cyber environment. However, cyber threat intelligence usually exists in an unstructured form, and a huge amount of data poses a great challenge to security analysts. To this end, this paper proposes a novel threat intelligence information extraction system combining multiple models, which contains four key steps: entity extraction, coreference resolution, relation extraction, and knowledge graph construction. In the entity extraction task, a multihead self-attention mechanism is adopted to extract the dependency relationships between words. In the coreference resolution task, contextual information and mention embedding are fused to improve the mention representation. Meanwhile, features of different dimensions are extracted using a convolutional neural network. In the relation extraction task, additional features such as part of speech, mention width, entity type, and distance of entity pairs are incorporated to improve the embedding representation. Finally, a knowledge graph is constructed to explicitly present entities and their relationships. Experimental results indicate that compared with the baseline model, the F1 score of our model is improved by at least 8.87, 9.82, and 10.56 on entity extraction, coreference resolution, and relation extraction, respectively. The knowledge graph in Neo4j demonstrates the effectiveness of our system.

Dynamic Editing Distance-based Extracting Relevant Information Approach from Social Networks

Online social networks, such as Facebook, Twitter, LinkedIn, etc., have grown exponentially in recent times with a large amount of information. These social networks have huge volumes of data especially in structured, textual, and unstructured forms which have often led to cyber-crimes like cyber terrorism, cyber bullying, etc., and extracting information from these data has now become a serious challenge in order to ensure the data safety. In this work, we propose a new, supervised approach for Information Extraction (IE) from Web resources based on remote dynamic editing, called EIDED. Our approach is part of the family of IE approaches based on masks extraction and is articulated around three algorithms: (i) a labeling algorithm, (ii) a learning and inference algorithm, and (iii) an extended edit distance algorithm. Our proposed approach is able to work even in the presence of anomalies in the tuples such as missing attributes, multivalued attributes, permutation of attributes, and in the structure of web pages. The experimental study, which we conducted, on a standard database of web pages, shows the performance of our EIDED approach compared to approaches based on the classic edit distance, and this with respect to the standard metrics recall coefficient, precision, and F1-measure.

“Operating Room Effectiveness”: Phenomenological Qualitative Study In Hospital

This study investigates the experience of health and medical personnel in hospital operating rooms and to evaluate the effectiveness of activities in operating rooms and ventilation conditions in hospitals. This study was designed as a qualitative research based on Heidegger's Hermeneutic Phenomenology Approach. The research method was carried out on health and medical personnel working in the operating room (OR) as observers. The research data were collected using an open and unstructured interview form. This form includes items that question the operating room design and operating room ventilation conditions. Data analysis used the Miles & Huberman method with the stages of data reduction, data display, and conclusion or verification. The research findings were obtained from seven informants (health and medical personnel) consisting of three men and four women. Age ranged from 30 to 45 years. Two themes and twelve sub-themes were determined as a result of data analysis. The research themes are (1) the effectiveness of operating room layout and (2) the effectiveness of operating room ventilation. This study concludes and provides important information about the physical requirements of the hospital operating room and the effectiveness for working professionally experience workers in the operating room (OR). It is understood that the room layout in the hospital is very good in helping smooth preoperative and postoperative handling. And operating room ventilation already supports good air circulation and is easy to control its use. But still found another room that causes noise. Hospital management must consider the type of activity, use of equipment, and/or other sources of noise, whether in the operating room building, ventilation of the operating room without a positive pressure system. So, the hospital must be able to pay attention to air humidity and pay attention to the state of the ventilation system and air filtration in the operating room so as to reduce the risk of preoperative and postoperative wound infections.

Marathi SentiWordNet: A lexical resource for sentiment analysis of Marathi

SummarySentiment analysis (SA) is information retrieval and computational linguistics that expresses sentiment about a specific document. Recently, research on SA has received greater interests due to the vast expansion of internet data. The comments and reviews posted online creates difficulties to the researchers, because the comments posted are written in unstructured formats with informal expressions, languages and possibly mixed languages. Most of the works on SA have been focusing only on English language but with respect to Indian languages, few works are done, especially on Hindi, Telugu, Bengali, and so forth. SentiWordNet is a useful lexical resource for determining the sentiment of a document or piece of text. There is currently no lexical resource for the Marathi language used for sentiment analysis. Therefore, this proposed research presents SA in one of the languages of Indian origin, that is, Marathi. In this paper, a new method is proposed for the development of a Marathi SentiWordNet (M‐SWN), synset‐based expansion approach using Marathi WordNet, which consists of Positive, Negative, and Neutral Polarity score, mapped from Hindi‐SentiWordNet (H‐SWN). Finally, sentiment analysis of movie reviews is carried out using M‐SWN using Bi‐LSTM based SVM classifier model.

A Review on Unstructured Data Processing in Hybrid Cloud Platform

Cloud storage systems are widely employed in many applications due to their improvement in cost, storage availability and security. Hybrid cloud platform refers to the architecture of a cloud system that combines more than one computing environments at a time. It can be either with one public and one private platform or the combination of two private or two public platforms. The hybrid cloud platform has the ability to share the information among the connected systems and that can be processed parallelly while accessing the data. The data that are stored in cloud platforms are mostly in unstructured format that could not be used for any applications like prediction, recommendation, and estimations. This paper reviews the attainments of the previous works that were used for data distribution and partitioning in a hybrid cloud platform, by ensuring the privacy and security of the stored data. The work also explores the future directions on the unstructured data processing by summarizing the research issues observed from the review analysis.

Sentiment Analysis of Covid-19 Tweets using Various Machine Learning Techniques with Hyperparameter Tuning on Twitter Database

People around the world are now being affected by the 2019 coronavirus disease (COVID-19) epidemic. People are using social media to express their opinions and general thoughts about the epidemic, which has affected their daily lives both in general and during lockdown periods. Twitter, one of the most used social media platforms, has seen a massive surge in tweets related to the coronavirus, including happy, sad, fear, and anger tweets, in a very short period. In the COVID-19 tweet dataset, there is a total number of tweets count is 179108 data in unstructured form, and after that preprocess that data to become semi structured form. After preprocessing feature extraction is used by applying CountVecotorizer and TF-IDF methods. Various Machine Learning Models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Stochastic Gradient Decent (SGD) are considered machine learning methods for sentiment analysis, To improve performance (accuracy), with Hyperparameters tuning method is used.

Volunteer Movement in Germany (Current Aspects)

This article covers the topics of volunteer movement, its social essence and significance in the modern world, as well as the shifts in its concept as a response to global social and cultural changes in the 21th century. This study is conducted using historical data and current experience of the volunteer movement in Germany. The goal of this article is to define the organizational specifics and nature of modern volunteering, based on the present-day reality and its most significant global challenges, as well as to summarise the latest experiences of volunteer movement in Germany associated with the forced mass migration of Ukrainian refugees. The results of the study made it possible to outline some guidelines for the non-governmental organisations that provide social support to vulnerable populations, in particular, people who have been exposed to a trauma caused by life-threatening events. To achieve the goal, such research methods as analysis and synthesis, generalisation, comparison, logical analysis method, survey, content analysis were used. Special attention has been paid to the support provided to refugees fleeing from countries where hostilities are taking place (on the example of refugees from Ukraine). The article uses data from special monitoring surveys of Ukrainian refugees conducted by the Institute for Economic Research (Germany), as well as the author’s own experience of volunteer work in Munich. In addition, as an illustration to the topic, the results of a small online survey of Ukrainian refugees are presented. The participants of the survey were women who were forced to leave Ukraine for Germany due to hostilities and received support from volunteers — the owners of local hotel businesses. The source of data on the number and composition of volunteers in Germany is a survey that has been carried out every 5 years since 1999. The 2019 study covered 27.7 thousand people aged 14 years and older. Based on the data, it was possible to draw conclusions regarding the advantages and disadvantages of the modern volunteering movement in Germany, as well as provide practical guidelines, which are primarily aimed at improving the effectiveness of volunteer work. In particular, the authors have identified the main features of modern transformations of volunteering. These transformations are characterized by transition from associative, unstructured forms to well-organized and highly specialized ones. The changes also affected the motivational characteristics of volunteers — the transition from altruism to pragmatism. One of the conclusions of the study is the identification of conflicting assessments of such changes: along with an increase in the effectiveness of volunteer work, its complication and excessive bureaucratization are observed. As a result, a negative attitude towards such work may be formed. The conclusions of this article can be useful for increasing the effectiveness of volunteer organizations in other countries, as well as scaling up experience through greater specialization, increasing the professional level of volunteers, and expanding activities.