Alexandria: Extensible Framework for Rapid Exploration of Social Media

The Alexandria system under development at IBM Research provides an extensible framework and platform for supporting a variety of big-data analytics and visualizations. The system is currently focused on enabling rapid exploration of text-based social media data. The system provides tools to help with constructing "domain models" (i.e., families of keywords and extractors to enable focus on tweets and other social media documents relevant to a project), to rapidly extract and segment the relevant social media and its authors, to apply further analytics (such as finding trends and anomalous terms), and visualizing the results. The system architecture is centered around a variety of REST-based service APIs to enable flexible orchestration of the system capabilities; these are especially useful to support knowledge-worker driven iterative exploration of social phenomena. The architecture also enables rapid integration of Alexandria capabilities with other social media analytics system, as has been demonstrated through an integration with IBM Research's SystemG. This paper describes a prototypical usage scenario for Alexandria, along with the architecture and key underlying analytics.Comment: 8 page

An End-to-End Time Series Model for Simultaneous Imputation and Forecast

Time series forecasting using historical data has been an interesting and challenging topic, especially when the data is corrupted by missing values. In many industrial problem, it is important to learn the inference function between the auxiliary observations and target variables as it provides additional knowledge when the data is not fully observed. We develop an end-to-end time series model that aims to learn the such inference relation and make a multiple-step ahead forecast. Our framework trains jointly two neural networks, one to learn the feature-wise correlations and the other for the modeling of temporal behaviors. Our model is capable of simultaneously imputing the missing entries and making a multiple-step ahead prediction. The experiments show good overall performance of our framework over existing methods in both imputation and forecasting tasks

Business artifacts with guard-stage-milestone lifecyclesProceedings of the 5th ACM international conference on Distributed event-based system - DEBS '11

none12Richard Hull;Anil Nigam;Piwadee Noi Sukaviriya;Roman Vaculin;Elio Damaggio;Riccardo De Masellis;Fabiana Fournier;Manmohan Gupta;Fenno Terry Heath;Stacy Hobson;Mark Linehan;Sridhar MaraduguRichard, Hull; Anil, Nigam; Piwadee Noi, Sukaviriya; Roman, Vaculin; Elio, Damaggio; De Masellis, Riccardo; Fabiana, Fournier; Manmohan, Gupta; Fenno Terry, Heath; Stacy, Hobson; Mark, Linehan; Sridhar, Maradug