We present CrowdFill, a system for collecting structured data from the crowd. While a typical microtask-based approach would pose specific questions to each worker and assemble the answers, CrowdFill shows a partially-filled table to all participating workers. Workers contribute by filling in empty cells, as well as upvoting and downvoting data entered by other workers. The system's synchronization scheme, based on a careful model of primitive operations, enables workers to collaboratively complete the table without latency overhead. CrowdFill allows the specification of constraints on the collected data, and has mechanisms for resolving inconsistencies. Its compensation scheme takes into account each worker's contribution to the final table, and the varying difficulty of data entry tasks. The paper includes some preliminary experimental results.

Sharing resources of a single database server among multiple tenants is common in multi-tenant Database-as-a-Service providers, such as Microsoft SQL Azure. Multi-tenancy enables cost reduction for the cloud service provider which it can pass on as savings to the tenants. However, resource sharing can adversely affect a tenant's performance due to other tenants' workloads contending for shared resources. Service providers today do not provide any assurances to a tenant in terms of isolating its performance from other co-located tenants. SQLVM, a project at Microsoft Research, is an abstraction for performance isolation which is built on a promise of reserving key database server resources, such as CPU, I/O and memory, for each tenant. The key challenge is in supporting this abstraction within a RDBMS without statically allocating resources to tenants, while ensuring low overheads and scaling to large numbers of tenants. This demonstration will show how SQLVM can effectively isolate a tenant's performance from other tenant workloads co-located at the same database server. Our demonstration will use various scripted scenarios and a data collection and visualization framework to illustrate performance isolation using SQLVM.

Deco is a comprehensive system for answering declarative queries posed over stored relational data together with data obtained on-demand from the crowd. In this paper we describe Deco's cost-based query optimizer, building on Deco's data model, query language, and query execution engine presented earlier. Deco's objective in query optimization is to find the best query plan to answer a query, in terms of estimated monetary cost. Deco's query semantics and plan execution strategies require several fundamental changes to traditional query optimization. Novel techniques incorporated into Deco's query optimizer include a cost model distinguishing between "free" existing data versus paid new data, a cardinality estimation algorithm coping with changes to the database state during query execution, and a plan enumeration algorithm maximizing reuse of common subplans in a setting that makes reuse challenging. We experimentally evaluate Deco's query optimizer, focusing on the accuracy of cost estimation and the efficiency of plan enumeration.

Crowdsourcing enables programmers to incorporate "human computation" as a building block in algorithms that cannot be fully automated, such as text analysis and image recognition. Similarly, humans can be used as a building block in data-intensive applications--providing, comparing, and verifying data used by applications. Building upon the decades-long success of declarative approaches to conventional data management, we use a similar approach for data-intensive applications that incorporate humans. Specifically, declarative queries are posed over stored relational data as well as data computed on-demand from the crowd, and the underlying system orchestrates the computation of query answers.

Given a large set of data items, we consider the problem of filtering them based on a set of properties that can be verified by humans. This problem is commonplace in crowdsourcing applications, and yet, to our knowledge, no one has considered the formal optimization of this problem. (Typical solutions use heuristics to solve the problem.) We formally state a few different variants of this problem. We develop deterministic and probabilistic algorithms to optimize the expected cost (i.e., number of questions) and expected error. We experimentally show that our algorithms provide definite gains with respect to other strategies. Our algorithms can be applied in a variety of crowdsourcing scenarios and can form an integral part of any query processor that uses human computation.

Deco is a system that enables declarative crowdsourcing: answering SQL queries posed over data gathered from the crowd as well as existing relational data. Deco implements a novel push-pull hybrid execution model in order to support a flexible data model and a precise query semantics, while coping with the combination of latency, monetary cost, and uncertainty of crowdsourcing. We demonstrate Deco using two crowdsourcing platforms: Amazon Mechanical Turk and an in-house platform, to show how Deco provides a convenient means of collecting and querying crowdsourced data.