With the rise of linked data and knowledge graphs, the need becomes compelling to find suitable solutions to increase the coverage and correctness of datasets, to add missing knowledge and to identify and remove errors. Several approaches – mostly relying on machine learning and NLP techniques – have been proposed to address this refinement goal; they usually need a partial gold standard, i.e. some “ground truth” to train automatic models. Gold standards are manually constructed, either by involving domain experts or by adopting crowdsourcing and human computation solutions.
Among the different human computation approaches, games with a purpose @cite_18 have experienced a wide success, because of their ability to engage users through the incentive of fun. A GWAP is a gaming application that exploits players' actions in the game to solve some (hidden) tasks; users play the game for fun, but the collateral effect'' of their playing is that the comparison and aggregation of players' contributions are used to solve some problems, usually labelling, classification, ranking, clustering, etc. Also in the Semantic Web community, GWAPs have been adopted to solve a number of linked data management issues @cite_12 @cite_11 @cite_4 @cite_29 @cite_20 @cite_16 @cite_1 @cite_5 @cite_28 , from multimedia labelling to ontology alignment, from error detection to link ranking.
@cite_18 @cite_4 @cite_28 @cite_29 @cite_1 @cite_16 @cite_5 @cite_20 @cite_12 @cite_11
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Human computation can address complex computational problems by tapping into large resource pools for relatively little cost. Two prominent human-computation techniques - games with a purpose (GWAP) and microtask crowdsourcing - can help resolve semantic-technology-related tasks, including knowledge representation, ontology alignment, and semantic annotation. To evaluate which approach is better with respect to costs and benefits, the authors employ categorization challenges in Wikipedia to ultimately create a large, general-purpose ontology. They first use the OntoPronto GWAP, then replicate its problem-solving setting in Amazon Mechanical Turk, using a similar task-design structure, evaluation mechanisms, and input data. We introduce autonomous gossiping (A G), a new genre epidemic algorithm for selective dissemination of information in contrast to previous usage of epidemic algorithms which flood the whole network. A G is a paradigm which suits well in a mobile ad-hoc networking (MANET) environment because it does not require any infrastructure or middleware like multicast tree and (un)subscription maintenance for publish subscribe, but uses ecological and economic principles in a self-organizing manner in order to achieve any arbitrary selectivity (flexible casting). The trade-off of using a stateless self-organizing mechanism like A G is that it does not guarantee completeness deterministically as is one of the original objectives of alternate selective dissemination schemes like publish subscribe. We argue that such incompleteness is not a problem in many non-critical real-life civilian application scenarios and realistic node mobility patterns, where the overhead of infrastructure maintenance may outweigh the benefits of completeness, more over, at present there exists no mechanism to realize publish subscribe or other paradigms for selective dissemination in MANET environments. Since its introduction at CHI 2004, the ESP Game has inspired many similar games that share the goal of gathering data from players. This paper introduces a new mechanism for collecting labeled data using "games with a purpose." In this mechanism, players are provided with either the same or a different object, and asked to describe that object to each other. Based on each other's descriptions, players must decide whether they have the same object or not. We explain why this new mechanism is superior for input data with certain characteristics, introduce an enjoyable new game called "TagATune" that collects tags for music clips via this mechanism, and present findings on the data that is collected by this game. Big Data applications suffer from unpredictable and unacceptably high pause times due to Garbage Collection (GC). This is the case in latency-sensitive applications such as on-line credit-card fraud detection, graph-based computing for analysis on social networks, etc. Such pauses compromise latency requirements of the whole application stack and result from applications' aggressive buffering caching of data, exposing an ill-suited GC design, which assumes that most objects will die young and does not consider that applications hold large amounts of middle-lived data in memory. To avoid such pauses, we propose NG2C, a new GC algorithm that combines pretenuring with user-defined dynamic generations. By being able to allocate objects into different generations, NG2C is able to group objects with similar lifetime profiles in the same generation. By allocating objects with similar lifetime profiles close to each other, i.e. in the same generation, we avoid object promotion (copying between generations) and heap fragmentation (which leads to heap compactions) both responsible for most of the duration of HotSpot GC pause times. NG2C is implemented for the OpenJDK 8 HotSpot Java Virtual Machine, as an extension of the Garbage First GC. We evaluate NG2C using Cassandra, Lucene, and GraphChi with three different GCs: Garbage First (G1), Concurrent Mark Sweep (CMS), and NG2C. Results show that NG2C decreases the worst observable GC pause time by up to 94.8 for Cassandra, 85.0 for Lucene and 96.45 for GraphChi, when compared to current collectors (G1 and CMS). In addition, NG2c has no negative impact on application throughput or memory usage.