The development of ontologies involves continuous but relatively small modifications. Even after a number of changes, ontology and its previous versions usually share most of their axioms. For large and complex ontologies this may require a few minutes, or even a few hours. Cognitive on a Web scale becomes increasingly stimulating because of the large volume of data involved and the complexity ...

Big data is large volume, heterogeneous, distributed data. Big data applications where data collection has grown continuously, it is expensive to manage, capture or extract and process data using existing software tools. For example Weather Forecasting, Electricity Demand Supply, social media and so on. With increasing size of data in data warehouse it is expensive to perform data analysis. Dat...

Biological sequence data can be subjected to a variety of analysis workflows to glean pertinent scientific insight. Recent advances in sequencing techniques have led to a deluge of biosequence data, which necessitates the use of high-performance computing resources in order to carry out analysis in a reasonable period of time. The tasks involved in creating and managing these computational jobs...

Analytics on Big Data is critical to derive business insights and drive innovation in today’s Internet companies. Such analytics involve complex computations on large datasets, and are typically performed on MapReduce based frameworks such as Hive and Pig. However, in our experience, these systems are still quite limited in performing at scale. In particular, calculations that involve complex j...

Experience from an operational Map-Reduce cluster reveals that outliers signi cantly prolong job completion. ˆe causes for outliers include run-time contention for processor, memory and other resources, disk failures, varying bandwidth and congestion along network paths and, imbalance in task workload. We present Mantri, a system that monitors tasks and culls outliers using causeand resource-aw...

An intention of MapReduce Sets for External Source Output expressions analysis has to suggest criteria how External Source Output expressions in External Source Output data can be defined in a meaningful way and how they should be compared. Similitude based MapReduce Sets for External Source Output Expression Analysis and MapReduce Sets for Assignment is expected to adhere to fundamental princi...

Large amount of parallel corpora is required for building Statistical Machine Translation (SMT) systems. We describe the TransDoop system for gathering translations to create parallel corpora from online crowd workforce who have familiarity with multiple languages but are not expert translators. Our system uses a Map-Reduce-like approach to translation crowdsourcing where sentence translation i...

The Map Reduce paradigm is now considered a standard platform that is used for large-scale data processing and management. A major operation that the Map Reduce platform relies on greatly is tasks scheduling. Although many schedulers have been presented, task scheduling is still one of the major problems that face Map Reduce frameworks. Schedulers need to maintain data locality to achieve an ac...