ABSTRACT To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.

A Sequence OLAP (S-OLAP) system provides a platform on which pattern-based aggregate (PBA) queries on a sequence database are evaluated. In its simplest form, a PBA query consists of a pat-tern template T and an aggregate function F. A pattern template is a sequence of variables, each is defined over a domain. For ex-ample, the template T = (X,Y,Y,X) consists of two variables X and Y. Each variable is instantiated with all possible values in its corresponding domain to derive all possible patterns of the tem-plate. Sequences are grouped based on the patterns they possess. The answer to a PBA query is a sequence cuboid (s-cuboid), which is a multidimensional array of cells. Each cell is associated with a pattern instantiated from the query’s pattern template. The value of each s-cuboid cell is obtained by applying the aggregate function F to the set of data sequences that belong to that cell. Since a pattern template can involve many variables and can be arbitrarily long, the induced s-cuboid for a PBA query can be huge. For most ana-lytical tasks, however, only iceberg cells with very large aggregate values are of interest. This paper proposes an efficient approach to identify and evaluate iceberg cells of s-cuboids. Experimental re-sults show that our algorithms are orders of magnitude faster than existing approaches.