Raghava Mutharaju

Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences

We present work in the spatio-temporal-thematic analysis of citizen-sensor observations pertaining to real-world events. Using Twitter as a platform for obtaining crowd-sourced observations, we explore the interplay between the 3 dimensions in extracting insightful summaries of observations. We present our experiences in building a web mashup application, Twitris [1] that also facilitates the spatio-temporal-thematic exploration of social signals underlying events.

Reasoning with fuzzy- ℇL + ontologies using MapReduce

Fuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy knowledge. In this paper, we consider fuzzy-ℇL+, which is a fuzzy extension of ℇL+. We first present revised completion rules for fuzzy-ℇL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-ℇL+ ontologies based on MapReduce.

Distributed and Scalable OWL EL Reasoning

OWL 2 EL is one of the tractable profiles of the Web Ontology Language OWL which is a W3C-recommended standard. OWL 2 EL provides sufficient expressivity to model large biomedical ontologies as well as streaming data such as traffic, while at the same time allows for efficient reasoning services. Existing reasoners for OWL 2 EL, however, use only a single machine and are thus constrained by memory and computational power. At the same time, the automated generation of ontological information from streaming data and text can lead to very large ontologies which can exceed the capacities of these reasoners. We thus describe a distributed reasoning system that scales well using a cluster of commodity machines. We also apply our system to a use case on city traffic data and show that it can handle volumes which cannot be handled by current single machine reasoners.

Very large scale OWL reasoning through distributed computation

Due to recent developments in reasoning algorithms of the various OWL profiles, the classification time for an ontology has come down drastically. For all of the popular reasoners, in order to process an ontology, an implicit assumption is that the ontology should fit in primary memory. The memory requirements for a reasoner are already quite high, and considering the ever increasing size of the data to be processed and the goal of making reasoning Web scale, this assumption becomes overly restrictive. In our work, we study several distributed classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.

Reasoning with Large Scale OWL 2 EL Ontologies Based on MapReduce

OWL 2 EL, which is underpinned by the description logic \(\mathcal {EL}\), has been used to build terminological ontologies in real applications, like biomedicine, multimedia and transportation. On the other hand, there have been techniques that allow developers and users acquiring large scale ontologies by automatically extracting data from different sources or integrating different domain ontologies. Thus the issue of handling large scale ontologies has to be tackled. In this short paper, we report our work on classification of OWL 2 EL ontologies using MapReduce, which is a distributed computing model for data processing. We discuss the main problems when we use MapReduce to handle OWL 2 EL classification and how we address these problems. We implement the algorithm using Hadoop, and evaluate it on a cluster of machines. The experimental results show that our prototype system achieves a linear scalability on large scale ontologies.

Towards a Rule Based Distributed OWL Reasoning Framework

The amount of data exposed in the form of RDF and OWL continues to increase exponentially. Some approaches have already been proposed for the scalable reasoning over several language profiles such as RDFS, OWL Horst, OWL 2 EL, OWL 2 RL etc. But all those approaches are limited to the particular ruleset that the reasoner supports. In this work, we propose the idea for a rule-based distributed reasoning framework that can support any given ruleset and highlight some of the challenges that needs to be solved in order to implement such a framework.

Social Signal Processing for Real-Time Situational Understanding: A Vision and Approach

The US Army Research Laboratory (ARL) and the Air Force Research Laboratory (AFRL) have established a collaborative research enterprise referred to as the Situational Understanding Research Institute (SURI). The goal is to develop an information processing framework to help the military obtain real-time situational awareness of physical events by harnessing the combined power of multiple sensing sources to obtain insights about events and their evolution. It is envisioned that one could use such information to predict behaviors of groups, be they local transient groups (e.g., Protests) or widespread, networked groups, and thus enable proactive prevention of nefarious activities. This paper presents a vision of how social media sources can be exploited in the above context to obtain insights about events, groups, and their evolution.