No abstract

No abstract

No abstract

We tackle the problems of semiautomatically matching linked data sets and of linking large collections of Web pages to linked data. Our system, ZenCrowd, (1) uses a three-stage blocking technique in order to obtain the best possible instance matches while minimizing both computational complexity and latency, and (2) identifies entities from natural language text using state-of-the-art techniques and automatically connects them to the linked open data cloud. First, we use structured inverted indices to quickly find potential candidate results from entities that have been indexed in our system. Our system then analyzes the candidate matches and refines them whenever deemed necessary using computationally more expensive queries on a graph database. Finally, we resort to human computation by dynamically generating crowdsourcing tasks in case the algorithmic components fail to come up with convincing results. We integrate all results from the inverted indices, from the graph database and from the crowd using a probabilistic framework in order to make sensible decisions about candidate matches and to identify unreliable human workers. In the following, we give an overview of the architecture of our system and describe in detail our novel three-stage blocking technique and our probabilistic decision framework. We also report on a series of experimental results on a standard data set, showing that our system can achieve a 95 % average accuracy on

We tackle the problem of entity linking for large collections of online pages; Our system, ZenCrowd, identifies entities from natural language text using state of the art techniques and automatically connects them to the Linked Open Data cloud. We show how one can take advantage of human intelligence to improve the quality of the links by dynamically generating micro-tasks on an online crowdsourcing platform. We develop a probabilistic framework to make sensible decisions about candidate links and to identify unreliable human workers. We evaluate ZenCrowd in a real deployment and show how a combination of both probabilistic reasoning and crowdsourcing techniques can significantly improve the quality of the links, while limiting the amount of work performed by the crowd.

Micro-task crowdsourcing has become a popular approach to e↵ectively tackle complex data management problems such as data linkage, missing values, or schema matching. However, the backend crowdsourced operators of crowd-powered systems typically yield higher latencies than the machineprocessable operators, this is mainly due to inherent efficiency di↵erences between humans and machines. This problem can be further exacerbated by the lack of workers on the target crowdsourcing platform, or when the workers are shared unequally among a number of competing requesters; including the concurrent users from the same organization who execute crowdsourced queries with di↵erent types, priorities and prices. Under such conditions, a crowd-powered system acts mostly as a proxy to the crowdsourcing platform, and hence it is very di cult to provide e ency guarantees to its end-users.Scheduling is the traditional way of tackling such problems in computer science, by prioritizing access to shared resources. In this paper, we propose a new crowdsourcing system architecture that leverages scheduling algorithms to optimize task execution in a shared resources environment, in this case a crowdsourcing platform. Our study aims at assessing the e ciency of the crowd in settings where multiple types of tasks are run concurrently. We present extensive experimental results comparing i) di↵erent multi-tenant crowdsourcing jobs, including a workload derived from real traces, and ii) di↵erent scheduling techniques tested with real crowd workers. Our experimental results show that task scheduling can be leveraged to achieve fairness and reduce query latency in multi-tenant crowd-powered systems, although with very di↵erent tradeo↵s compared to traditional settings not including human factors. General Terms

No abstract

The continuous shift towards data-driven approaches to business, and a growing attention to improving return on investments (ROI) for cluster infrastructures is generating new challenges for big-data frameworks. Systems originally designed for big batch jobs now handle an increasingly complex mix of computations. Moreover, they are expected to guarantee stringent SLAs for production jobs and minimize latency for best-effort jobs.In this paper, we introduce reservation-based scheduling, a new approach to this problem. We develop our solution around four key contributions: 1) we propose a reservation definition language (RDL) that allows users to declaratively reserve access to cluster resources, 2) we formalize planning of current and future cluster resources as a Mixed-Integer Linear Programming (MILP) problem, and propose scalable heuristics, 3) we adaptively distribute resources between production jobs and best-effort jobs, and 4) we integrate all of this in a scalable system named Rayon, that builds upon Hadoop / YARN.We evaluate Rayon on a 256-node cluster against workloads derived from Microsoft, Yahoo!, Facebook, and Cloudera's clusters. To enable practical use of Rayon, we opensourced our implementation as part of Apache Hadoop 2.6.