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See detailAsynchronous Stream Data Processing using a Light-Weight and High-Performance Dataflow Engine
Ellampallil Venugopal, Vinu UL; Theobald, Martin UL

Presentation (2020, December 11)

Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous ... [more ▼]

Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous progress in distributed stream processing systems in the past few years, the high-throughput and low-latency (a.k.a. high sustainable-throughput) requirement of modern applications is pushing the limits of traditional data processing infrastructures. This paper introduces a new distributed stream data processing engine (DSPE), called “Asynchronous Iterative Routing” or simply AIR, which implements a light-weight, dynamic sharding protocol. AIR expedites a direct and asynchronous communication among all the worker nodes via multiple Message Passing Interface (MPI) communication channels and thereby completely avoids any additional communication overhead with a dedicated master node. With its unique design, AIR scales out to clusters consisting of up to 8 nodes and 224 cores, performing much better than existing DSPEs, and it performs up to 15 times better than Spark and Flink in terms of sustainable-throughput. [less ▲]

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See detailAIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing
Ellampallil Venugopal, Vinu UL; Theobald, Martin UL; Chaychi, Samira UL et al

in AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing (2020, September 01)

Distributed Stream Processing Engines (DSPEs) are currently among the most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow ... [more ▼]

Distributed Stream Processing Engines (DSPEs) are currently among the most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow programs and big data analytics. In this paper, we describe the architecture of our AIR engine, which is designed from scratch in C++ using the Message Passing Interface (MPI), pthreads for multithreading, and is directly deployed on top of a common HPC workload manager such as SLURM. AIR implements a light-weight, dynamic sharding protocol (referred to as “Asynchronous Iterative Routing”), which facilitates a direct and asynchronous communication among all worker nodes and thereby completely avoids any additional communication overhead with a dedicated master node. With its unique design, AIR fills the gap between the prevalent scale-out (but Java-based) architectures like Apache Spark and Flink, on one hand, and recent scale-up (and C++ based) prototypes such as StreamBox and PiCo, on the other hand. Our experiments over various benchmark settings confirm that AIR performs as good as the best scale-up SPEs on a single-node setup, while it outperforms existing scale-out DSPEs in terms of processing latency and sustainable throughput by a factor of up to 15 in a distributed setting. [less ▲]

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See detailDifficulty-level modeling of ontology-based factual questions
Ellampallil Venugopal, Vinu UL; Kumar, P Sreenivasa

in Semantic Web – Interoperability, Usability, Applicability (2020)

Semantics-based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions. Determining the difficulty-level of these system ... [more ▼]

Semantics-based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions. Determining the difficulty-level of these system-generated questions is helpful to effectively utilize them in various educational and professional applications. The existing approach for for predicting the difficulty-level of factual questions utilizes only few naive features and, its accuracy (F-measure) is found to be close to only 50% while considering our benchmark set of 185 questions. In this paper, we propose a new methodology for this problem by identifying new features and by incorporating an educational theory, related to difficulty-level of a question, called Item Response Theory (IRT). In the IRT, knowledge proficiency of end users (learners) are considered for assigning difficulty-levels, because of the assumptions that a given question is perceived differently by learners of various proficiency levels. We have done a detailed study on the features/factors of a question statement which could possibly determine its difficulty-level for three learner categories (experts, intermediates, and beginners). We formulate ontology-based metrics for the same. We then train three logistic regression models to predict the difficulty-level corresponding to the three learner categories. The output of these models is interpreted using the IRT to find a question’s overall difficulty-level. The accuracy of the three models based on cross-validation is found to be in satisfactory range (67-84%). The proposed model (containing three classifiers) outperforms the existing model by more than 20% in precision, recall and F1-score measures. [less ▲]

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See detailGuided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback
Wu, Yan; Chen, Jinchuan; Haxhidauti, Plarent et al

in Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback (2020, March 12)

Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both ... [more ▼]

Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both of these large-scale KBs achieve very high average precision values (above 95% for YAGO3), subtle mistakes in a few of the underlying extraction rules may still impose a substantial amount of systematic extraction mistakes for specific relations. For example, by applying the same regular expressions to extract person names of both Asian and Western nationality, YAGO erroneously swaps most of the family and given names of Asian person entities. For traditional rule-learning approaches based on Inductive Logic Programming (ILP), it is very difficult to detect these systematic extraction mistakes, since they usually occur only in a relatively small subdomain of the relations’ arguments. In this paper, we thus propose a guided form of ILP, coined “GILP”, that iteratively asks for small amounts of user feedback over a given KB to learn a set of data-cleaning rules that (1) best match the feedback and (2) also generalize to a larger portion of facts in the KB. We propose both algorithms and respective metrics to automatically assess the quality of the learned rules with respect to the user feedback. [less ▲]

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See detailBenchmarking Synchronous and Asynchronous Stream Processing Systems
Ellampallil Venugopal, Vinu UL; Theobald, Martin UL

in Ellampallil Venugopal, Vinu; Theobald, Martin (Eds.) Benchmarking Synchronous and Asynchronous Stream Processing Systems (2020, January 02)

Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous ... [more ▼]

Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous progress in distributed stream processing systems in the past few years, the high-throughput and low-latency (a.k.a. high sustainable-throughput) requirement of modern applications is pushing the limits of traditional data processing infrastructures. To understand the upper bound of the maximum sustainable throughput that is possible for a given node configuration, we have designed multiple hard-coded multi-threaded processes (called ad-hoc dataflows) in C++ using Message Passing Interface (MPI) and Pthread libraries. Our preliminary results show that our ad-hoc design on average is 5.2 times better than Flink and 9.3 times better than Spark. [less ▲]

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See detailImproving Ontology Verbalization using Semantic-level Refinement
Ellampallil Venugopal, Vinu UL; Kumar, P Sreenivasa

in Description Logics 2019: Oslo, Norway (2019, July 11)

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