Here, we present the characteristic elements of the EW-Shopp toolkit, an open source software ecosystem, capable of managing data in tabular format and of generating linked data to be used for analytics and visualization. The EW-Shopp toolkit covers the three main activities commonly identified in a data science project.
- Data preparation and enrichment. This activity can be carried out by means of three tools, namely DataGraft, ASIA, and ABSTAT. DataGraft and its data transformation tool Grafterizer provide data management, data cleaning, modelling, preparation and graph transformation functionalities using user-specified transformations. ASIA is a tool for the semantic enrichment of data available in tabular formats, thus helping users in integrating business data with events and weather data. Semantic reconciliation algorithms are integrated into a user interface to help users map the data schema to shared vocabularies and ontologies and link data values to shared systems of identifiers. Data enrichment widgets exploit these links to shared systems of identifiers to ease the extraction of additional data from third-party sources and their fusion into the original tabular data. ABSTAT is a tool to profile knowledge graphs represented in RDF based on linked data summarization mechanisms. The profiles extracted by ABSTAT describe the content of the knowledge graphs using abstraction (schema-level patterns) and statistics. The profiles help users understand the content of the knowledge graphs used in the platform (e.g., linked product data), support ASIA’s semantic reconciliation algorithms, and provide data quality insights. Learn more about EW-Shopp Data Preparation and Enrichment tools.
- Data visualization and navigation. The suite implements this functionality by providing tools for producing high-quality reports of the transformed, enriched and analyzed information obtained from the platform. Learn more about EW-Shopp Data Visualization and Navigation tools.
- Data Analysis. This functionality is implemented using QMiner data analytics platform and is opened to other solutions. Within the toolkit, QMiner provides functionality of learning models from historic datasets and use them for prediction on new data points. It implements a comprehensive set of techniques for supervised, unsupervised and active learning which support bot structured and unstructured data. Learn more about EW-Shopp Data Analytics tools.