There are several different definitions and kinds of case studies. Because of different reasons the case studies can be either single or multiple. This study attempts to answer when to write a single case study and when to write a multiple case study. It will further answer the benefits and disadvantages with the different types. The literature review, which is based on secondary sources, is about case studies. Then the literature review is discussed and analysed to reach a conclusion. The conclusion is that there are several different opinions if a single case study or a multiple case study is the best choice. Different causes to consider in the choice to make a single case study or a multiple case study are presented. Some causes are that the amount depends on the context, upon how much is known and how much new information the cases bring. Another conclusion from the case studies I looked among is that it is generally more number of pages in the multiple case studies than in the single case studies.

Single case studies vs. multiple case studies: A comparative study

Semantic Web in data mining and knowledge discovery

“Imagine an electronic page for each species of organism on Earth, available everywhere by single access on command. The page contains the scientific name of the species, a pictorial or genomic presentation of the primary type specimen on which its name is based, and a summary of its diagnostic traits. The page opens out directly or by linking to other databases, such as ARKive, Ecoport, GenBank and MORPHOBANK. It comprises a summary of everything known about the species’ genome, proteome, geographical distribution, phylogenetic position, habitat, ecological relationships and, not least, its perceived practical importance for humanity.

Encyclopedia of Life

Proceedings of the 8th International Semantic Web Conference

Organizational learning (OL) enables organizations to transform individual knowledge into organizational knowledge. Organizations struggle to implement practical approaches due to the lack of concr...

https://journals.sagepub.com/doi/pdf/10.1177/2158244018794224

Approaches for organizational learning: a literature review

In an interconnected world, Linked Data is more important than ever before. However, it is still quite dicult to access this new wealth of semantic data directly without having in-depth knowledge about SPARQL and related semantic technologies. Also, most people are currently used to consuming data as 2-dimensional tables. Linked Data is by denition always a graph, and not that many people are used to handle data in graph structures. Therefore we present the Linked Data Query Wizard, a web-based tool for displaying, accessing, ltering, exploring, and navigating Linked Data stored in SPARQL endpoints. The main innovation of the interface is that it turns the graph structure of Linked Data into a tabular interface and provides easy-to-use interaction possibilities by using metaphors and techniques from current search engines and spreadsheet applications that regular web users are already familiar with.

/pdf/linked-data-query-wizard-a-novel-interface-for-accessing-4m5z0u88nj.pdf

Linked Data Query Wizard: A Novel Interface for Accessing SPARQL Endpoints

Linked Data has become an essential part of the Semantic Web. A lot of Linked Data is already available in the Linked Open Data cloud, which keeps growing due to an influx of new data from research and open government activities. However, it is still quite difficult to access this wealth of semantically enriched data directly without having in-depth knowledge about SPARQL and related semantic technologies. In this paper, we present the Linked Data Query Wizard, a prototype that provides a Linked Data interface for non-expert users, focusing on keyword search as an entry point and a tabular interface providing simple functionality for filtering and exploration.

/pdf/linked-data-query-wizard-a-tabular-interface-for-the-ec7c02zoew.pdf

Linked Data Query Wizard: A Tabular Interface for the Semantic Web

Research papers are published in various digital libraries, which deploy their own meta-models and technologies to manage, query, and analyze scientific facts therein. Commonly they only consider the meta-data provided with each article, but not the contents. Hence, reaching into the contents of publications is inherently a tedious task. On top of that, scientific data within publications are hardcoded in a fixed format (e.g. tables). So, even if one manages to get a glimpse of the data published in digital libraries, it is close to impossible to carry out any analysis on them other than what was intended by the authors. More effective querying and analysis methods are required to better understand scientific facts. In this paper, we present the web-based CODE Visualisation Wizard, which provides visual analysis of scientific facts with emphasis on automating the visualisation process, and present an experiment of its application. We also present the entire analytical process and the corresponding tool chain, including components for extraction of scientific data from publications, an easy to use user interface for querying RDF knowledge bases, and a tool for semantic annotation of scientific data sets.

Suggesting visualisations for published data

Linked Open Data (LOD) is becoming an essential part of the Semantic Web. Although LOD has amassed large quantities of structured data from diverse, openly available data sources, there is still a lack of user-friendly interfaces and mechanisms for exploring this huge resource. In this paper we highlight two critical issues related to the exploration of the semantic LOD pool by end users. We introduce a proof of concept application which helps users to search information about a concept without having to know the mechanics of the Semantic Web or Linked Data. We assume that this kind of application may lead to bridge the gap between semantic search and end users. With this application, we concentrated on two aspects: 1) A novel Concept Aggregation Framework to present the most relevant information of LOD resources in an easy to understand way. 2) A simplified search mechanism which hides the complex underlying semantic search logic. This research is intended to simplify the LOD end user interfaces so that they may be used by novice users who don't possess any prior knowledge of semantic structures.

CAF-SIAL: Concept aggregation framework for structuring informational aspects of linked open data

The Semantic Web strives to add structure and meaning to the Web, thereby providing better results and easier interfaces for its users. One important foundation of the Semantic Web is Linked Data, the concept of interconnected data, describing resources by use of RDF and URIs. Linked Data (LOD) provides the opportunity to explore and combine datasets on a global scale -- something which has never been possible before. However, at its current stage, the Linked Data cloud yields little benefit for end users who know nothing of ontologies, triples and SPARQL. This paper presents an intelligent technique for locating desired URIs from the huge repository of Linked Data. Search keywords provided by users are utilized intelligently for locating the intended URI. The proposed technique has been applied in a simplified end user interface for LOD. The system evaluation shows that the proposed technique has reduced user's cognitive load in finding relevant information.

Turning keywords into URIs: simplified user interfaces for exploring linked data

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Patrick Hoefler

Author Tools

Chat about Author