Speakers
We lining up some of the most relevant industry leaders in Big Data for keynote sessions. The time limit to submit proposals is over so we will soon reveal the definitive list of speakers.
15:15 ~ 16:30
Sala 5
César de Pablo Sánchez
Senior Software Engineer Daedalus- Technical
A real time semantic search engine for social tv streams
Social TV, the use of social networks to comment on TV programs is a growing phenomena. TV channels and brands are turning into social networks to look for real time insights about their programs. Understanding the global conversation about a program is useful to acquire insights for broadcasters and brands. For broadcasters, acquiring insights while a program is aired enable them to produce new content formats that include social conversation. For brands, it helps to prevent reputation crisis and increase the reach of their marketing efforts. For viewers, which increasingly use second screen devices, should benefit from tools that help to understand opinions around main content and connect with peers during TV programs or live events.
We present a system that combines natural language processing (Textalytics API) and a scalable semi-structured database/search engine (senseiDB) to provide semantic and faceted search, real time analytics and support visualizations for this kind of applications.
In the first part, we will present some of the useful NLP methods that we can use to tame unstructured big data like Twitter or Facebook comments. We will include description for tasks like text categorization, sentiment analysis, named entity recognition. We would also see how this data could be related to external data like Linked Data points. While the description would be general, examples would be illustrated using Textalytics API.
Then we would present how this data could be ingested and made available for search in real time using a semi-structured database like SenseiDB. We would present key features of SenseiDB including high performance real time indexing and simultaneous querying, distribution and support for full-text and faceted search. We would also discuss how facets may be overused to provide real time analytics and enable semantic search. Finally we will discuss advantages, problems and current limitations of SenseiDB.
Takeaway Points.
- Analyzing and searching text in social streams
- Integrating text analytics services (Textalytics) and a semi-structured database (SenseiDB)
- Key features of SenseiDB
We present a system that combines natural language processing (Textalytics API) and a scalable semi-structured database/search engine (senseiDB) to provide semantic and faceted search, real time analytics and support visualizations for this kind of applications.
In the first part, we will present some of the useful NLP methods that we can use to tame unstructured big data like Twitter or Facebook comments. We will include description for tasks like text categorization, sentiment analysis, named entity recognition. We would also see how this data could be related to external data like Linked Data points. While the description would be general, examples would be illustrated using Textalytics API.
Then we would present how this data could be ingested and made available for search in real time using a semi-structured database like SenseiDB. We would present key features of SenseiDB including high performance real time indexing and simultaneous querying, distribution and support for full-text and faceted search. We would also discuss how facets may be overused to provide real time analytics and enable semantic search. Finally we will discuss advantages, problems and current limitations of SenseiDB.
Takeaway Points.
- Analyzing and searching text in social streams
- Integrating text analytics services (Textalytics) and a semi-structured database (SenseiDB)
- Key features of SenseiDB