Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. At the same time, the existing tools that attempt to automate the data cleaning procedure typically focus on a specific use case and operation. Still, even such specialized tools exhibit long running times or fail to process large datasets. Therefore, from a user's perspective, one is forced to use a different, potentially inefficient tool for each category of errors. This paper addresses the coverage and efficiency problems of data cleaning. It introduces CleanM ( pronounced clean'em ), a language which can express multiple types of cleaning operations. CleanM goes through a three-level translation process for optimization purposes; a different family of optimizations is applied in each abstraction level. Thus, CleanM can express complex data cleaning tasks, optimize them in a unified way, and deploy them in a scaleout fashion. We validate the applicability of CleanM by using it on top of CleanDB, a newly designed and implemented framework which can query heterogeneous data. When compared to existing data cleaning solutions, CleanDB a) covers more data corruption cases, b) scales better, and can handle cases for which its competitors are unable to terminate, and c) uses a single interface for querying and for data cleaning.

Today, flows crossing the Internet are not equally sensitive to loss or delay variations. For several years, a lot of research has been done to enhance the IP stack to solve the problem of the different needs of heterogeneous traffic. In the light of the frustrating experience of existing IP QoS approaches, we present a new differentiated service scheme called EDS : "Equivalent Differentiated Services". The EDS can be deployed incrementally and provides "different but equal services". This new scheme allows us to differentiate a number of N traffic classes on loss and delay without giving an absolute better service to anyone. This proposition represents a radical departure from traditional "DiffServ" architectures which rely on bounded domain concept and pricing models. In this paper we look at the goals of this "soft" quality of service architecture. We present the principles and implementation details of the EDS scheme. Finally we examine the transport layer issues and perspectives within our framework.

Presents a scalable protocol for conducting periodic probes of network performance in a way that minimizes collisions between separate probes. The goal of the protocol is to enable active performance monitoring of large-scale distributed computational systems and networks. We use the protocol to generate time series of measurement data that are then exposed to numerical forecasting models when a prediction of network performance is required. We present the protocol and demonstrate its effectiveness using the Network Weather Service -a tool for dynamically predicting network, CPU, memory and storage performance.