A contour plot approach that uses data accumulated during routine monitoring to obtain insights into lagoon behaviour

Regular monitoring of wastewater characteristics is undertaken on most wastewater treatment plants. The data acquired during this process are usually filed and forgotten. However, systematic analysis of these data can provide useful insights into plant behaviour. Conventional graphical techniques are inadequate to give a good overall picture of how wastewater characteristics vary, with time and along the lagoon system. An approach based on the use of contour plots was devised that largely overcomes this problem. Superimposition of contour plots for different parameters can be used to gain a qualitative understanding of the nature and strength of relationships between the parameters. This is illustrated in an analysis of monitoring data for lagoon 115 East at the Western Treatment Plant, near Melbourne, Australia. In this illustrative analysis, relationships between ammonia removal rates and parameters such as chlorophyll a level and temperature are explored using a contour plot superimposition approach. It is concluded that this approach can help improve our understanding, not only of lagoon systems, but of other wastewater treatment systems as well.

IJDWM Special Issue: Advances in Data Mining Applications

This special issue is a collection of the selected papers published on the proceedings of the First International Conference on Advanced Data Mining and Applications (ADMA) held in Wuhan, China in 2005. The articles focus on the innovative applications of data mining approaches to the problems that involve large data sets, incomplete and noise data, or demand optimal solutions.

A scaleless data model for direct and progressive spatial query processing

A progressive spatial query retrieves spatial data based on previous queries (e.g., to fetch data in a more restricted area with higher resolution). A direct query, on the other side, is defined as an isolated window query. A multi-resolution spatial database system should support both progressive queries and traditional direct queries. It is conceptually challenging to support both types of query at the same time, as direct queries favour location-based data clustering, whereas progressive queries require fragmented data clustered by resolutions. Two new scaleless data structures are proposed in this paper. Experimental results using both synthetic and real world datasets demonstrate that the query processing time based on the new multiresolution approaches is comparable and often better than multi-representation data structures for both types of queries.

Space efficient quantile summary for constrained sliding windows on a data stream

In many online applications, we need to maintain quantile statistics for a sliding window on a data stream. The sliding windows in natural form are defined as the most recent N data items. In this paper, we study the problem of estimating quantiles over other types of sliding windows. We present a uniform framework to process quantile queries for time constrained and filter based sliding windows. Our algorithm makes one pass on the data stream and maintains an E-approximate summary. It uses O((1)/(epsilon2) log(2) epsilonN) space where N is the number of data items in the window. We extend this framework to further process generalized constrained sliding window queries and proved that our technique is applicable for flexible window settings. Our performance study indicates that the space required in practice is much less than the given theoretical bound and the algorithm supports high speed data streams.

Dissecting query performance in logical data models: Parsimony vs. greater ontological clarity

Even when data repositories exhibit near perfect data quality, users may formulate queries that do not correspond to the information requested. Users’ poor information retrieval performance may arise from either problems understanding of the data models that represent the real world systems, or their query skills. This research focuses on users’ understanding of the data structures, i.e., their ability to map the information request and the data model. The Bunge-Wand-Weber ontology was used to formulate three sets of hypotheses. Two laboratory experiments (one using a small data model and one using a larger data model) tested the effect of ontological clarity on users’ performance when undertaking component, record, and aggregate level tasks. The results indicate for the hypotheses associated with different representations but equivalent semantics that parsimonious data model participants performed better for component level tasks but that ontologically clearer data model participants performed better for record and aggregate level tasks.