Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate the problem: In order to manipulate large sets of complex objects as efficiently as today's database systems manipulate simple records, query-processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and postrelational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set-matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

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Query evaluation techniques for large databases

The popularity of the World-Wide Web (WWW) has made it a prime vehicle for disseminating information. The relevance of database concepts to the problems of managing and querying this information has led to a significant body of recent research addressing these problems. Even though the underlying challenge is the one that has been traditionally addressed by the database community how to manage large volumes of data the novel context of the WWW forces us to significantly extend previous techniques. The primary goal of this survey is to classify the different tasks to which database concepts have been applied, and to emphasize the technical innovations that were required to do so.

Database techniques for the World-Wide Web: a survey

A method and apparatus are disclosed for managing a transaction log which contains updates representing operations performed on a database replica in a network of disconnectable computers. The invention provides for compression of the log by the identification and removal of redundant updates. Log compression removes apparent inconsistencies between operations performed on disconnected computers, reduces storage requirements on each computer, and speeds up transaction synchronization when the computers are reconnected. The invention also provides for restoration of prior versions of database objects using the log.

Transaction log management in a disconnectable computer and network

A method and apparatus are disclosed for synchronizing transactions in a disconnectable network. Each transaction includes operations that were performed on a database replica on one computer while that computer was disconnected from another computer and hence from that other computer's replica. Transaction synchronization, which occurs after the computers are reconnected, transfers information from each computer to the other computer and applies updates to both replicas as appropriate. Transaction logs and clash handling tools may be used with the invention.

Transaction synchronization in a disconnectable computer and network

The Semantic Web 10

SuperSQL is an extension of SQL that allows query results presented in various media for publishing and presentations with simple but sophisticated formatting capabilities. SuperSQL query can generate various kinds of materials, for example, a LaTeX source file to publish query results in a nested table, HTML or Java source files to present the result on WWW browsers, and other media including MS-Excel worksheet, Tcl/Tk, O2C, etc. O2C is a data manipulation language of O2 and thus useful to migrate data in a relational database to an object oriented database.SuperSQL is meant to provide a theoretical and practical foundation for 4GL-type applications such as report writers and DB/WWW coordinators.In this demonstration, we show how TFE reorganize the query results into various media in a universal way, first by grouping tuples according to an arbitrary tree structured schema, and by translating them with the constructors available in the target media.

SuperSQL: an extended SQL for database publishing and presentation

The continual advancement of internet technologies has led to the evolution of how individuals and organizations operate. For example, through the internet, we can now tap a remote workforce to help us accomplish certain tasks, a phenomenon called crowdsourcing. Crowdsourcing is an approach that relies on people to perform activities that are costly or time-consuming using traditional methods. Depending on the incentive given to the crowd workers, crowdsourcing can be classified as paid or unpaid. In paid crowdsourcing, the workers are incentivized financially, enabling the formation of a robust workforce, which allows fast completion of tasks. Consequently, in unpaid crowdsourcing, the lack of financial incentive potentially leads to an unpredictable workforce and indeterminable task completion time. However, since payment to workers is not necessary, it can be an economical alternative for individuals and organizations who are more concerned about the budget than the task turnaround time. In this study, we explore unpaid crowdsourcing by reviewing crowdsourcing applications where the crowd comes from a pool of volunteers. We also evaluate its performance in sentiment analysis and data extraction projects. Our findings suggest that for such tasks, unpaid crowdsourcing completes slower but yields results of similar or higher quality compared to its paid counterpart.

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An investigation of unpaid crowdsourcing

We study task composition in crowdsourcing and the effect of personalization and diversity on performance. A central process in crowdsourcing is task assignment, the mechanism through which workers find tasks. On popular platforms such as Amazon Mechanical Turk, task assignment is facilitated by the ability to sort tasks by dimensions such as creation date or reward amount. Task composition improves task assignment by producing for each worker, a personalized summary of tasks , referred to as a Composite Task (CT). We propose different ways of producing CTs and formulate an optimization problem that finds for a worker, the most relevant and diverse CTs . We show empirically that workers’ experience is greatly improved due to personalization that enforces an adequation of CTs with workers’ skills and preferences. We also study and formalize various ways of diversifying tasks in each CT. Task diversity is grounded in organization studies that have shown its impact on worker motivation  [33] . Our experiments show that diverse CTs contribute to improving outcome quality. More specifically, we show that while task throughput and worker retention are best with ranked lists, crowdwork quality reaches its best with CTs diversified by requesters, thereby confirming that workers look to expose their “good” work to many requesters.

Personalized and Diverse Task Composition in Crowdsourcing

Due to the amount of work needed in manual sentiment analysis of written texts, techniques in automatic sentiment analysis have been widely studied. However, compared to manual sentiment analysis, the accuracy of automatic systems range only from low to medium. In this study, we solve a sentiment analysis problem by crowdsourcing. Crowdsourcing is a problem solving approach that uses the cognitive power of people to achieve specific computational goals. It is implemented through an online platform, which can either be paid or volunteer-based. We deploy crowdsourcing applications in paid and volunteer-based platforms to classify teaching evaluation comments from students. We present a comparison of the results produced by crowdsourcing, manual sentiment analysis, and an existing automatic sentiment analysis system. Our findings show that the crowdsourced sentiment analysis in both paid and volunteer-based platforms are considerably more accurate than the automatic sentiment analysis algorithm but still fail to achieve high accuracy compared to the manual method. To improve accuracy, the effect of increasing the size of the crowd could be explored in the future.

Automatic vs. Crowdsourced Sentiment Analysis

Crowdsourcing has gained popularity in a variety of domains as an increasing number of jobs are "taskified" and completed independently by a set of workers. A central process in crowdsourcing is the mechanism through which workers find tasks. On popular platforms such as Amazon Mechanical Turk, tasks can be sorted by dimensions such as creation date or reward amount. Research efforts on task assignment have focused on adopting a requester-centric approach whereby tasks are proposed to workers in order to maximize overall task throughput, result quality and cost. In this paper, we advocate the need to complement that with a worker-centric approach to task assignment, and examine the problem of producing, for each worker, a personalized summary of tasks that preserves overall task throughput. We formalize task composition for workers as an optimization problem that finds a representative set of k valid and relevant Composite Tasks (CTs). Validity enforces that a composite task complies with the task arrival rate and satisfies the worker's expected wage. Relevance imposes that tasks match the worker's qualifications. We show empirically that workers' experience is greatly improved due to task homogeneity in each CT and to the adequation of CTs with workers' skills. As a result task throughput is improved.

https://hal.archives-ouvertes.fr/hal-01407780/document

Motomichi Toyama

Papers

SuperSQL: an extended SQL for database publishing and presentation

An investigation of unpaid crowdsourcing

Personalized and Diverse Task Composition in Crowdsourcing

Automatic vs. Crowdsourced Sentiment Analysis

Task Composition in Crowdsourcing