João Marcelo Borovina Josko

Avenida dos Estados, 5001 · Santo André · São Paulo · Brazil
Room 520-2 · Tower 2 · Building A


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Data Quality and Data Integration

Independent of your business, all data analysis initiative requires data at certain quality levels to produce reliable outcomes. So, huge amount of data persisted in any kind of DBMS is not enough. Data quality assessment and data integration complement each other to rise data quality to the required levels stablish by business. The former is in charge of detecting data defects on several types of data (e.g., atemporal, temporal, spatial, semi-structured, non-structured), while the latter attempts to identify integrations points between different entities or schemas. Both use a broad set of resources (e.g., machine learning, data visualizations, natural language processing, probability) to reach its aims.

Data Visualization

Data Visualization is interested in mixing together computer and human abilities to enable complex data analysis mediated to interactive visual representations. Visualizations are required for a myriad of situations, including data quality visual assessment, education, database administration, cloud administration, financial analysis, scientific analysis and much more. New or improved visualizations techniques (metaphors), New or improved interactive techniques, evaluation of visualizations solutions represent some of the promising works regarding this subject.

Computer Science Education

Computer science is a diverse field that gathers several technical, personal and in group skills. Learning such complex and interconnected skills demand much more than gis, blackboard and presentations. The use of pedagogical methods with or without support of software resources (e.g., simulators, intelligent interactive tutors, visualizations) are a must to leverage computer science learning.


Databases are ubiquitous to all kinds of relevant applications. Due to this relevance, databases require several procedures to work propely throughout its productive lifetime. Regarding development, database evolution (e.g., schema evolution or versioning ) represent a key aspect for both Relational and NoSQL DBMS.

Data Analytics

Possess data is not an advantage per se, but what you can learn from them. Different methods (e.g., supervised algorithms, RFM metrics) can be combined to leverage understanding about their Customers value and behavior (know as Database Marketing). This example of analysis can be easily replicated to other business challenges related to stock exchange market analysis, patient record analysis, historical medical procedures analysis, to name a few.