An approach to filtering duplicate RFID data streams

In a system where distributed network of Radio Frequency Identification (RFID) readers are used to collaboratively collect data from tagged objects, a scheme that detects and eliminates redundant data streams is required. To address this problem, we propose an approach that is based on Bloom filter to detect duplicate readings and filter redundant RFID data streams. We have evaluated the performance of the proposed approach and compared it with existing approaches. The experimental results demonstrate that the proposed approach provides superior performance as compared to the baseline approaches.

Knowledge discovery in data streams

Knowing what to do with the massive amount of data collected has always been an ongoing issue for many organizations. While data mining has been touted to be the solution, it has failed to deliver the impact despite its successes in many areas. One reason is that data mining algorithms were not designed for the real world, i.e., they usually assume a static view of the data and a stable execution environment where resources are abundant. The reality however is that data are constantly changing and the execution environment is dynamic. Hence, it becomes difficult for data mining to truly deliver timely and relevant results. Recently, the processing of stream data has received many attention. What is interesting is that the methodology to design stream-based algorithms may well be the solution to the above problem. In this entry, we discuss this issue and present an overview of recent works.

Wood to water : short-term effects of the re-introduction of wood to streams in agricultural environments

Rehabilitation of streams on agricultural properties has become a priority for landholders and managers in recent years in Australia. Fencing and re-vegetation of riparian zones are first priorities to improve riparian habitat values and biodiversity, however changes to in-stream habitat complexity are unlikely to result in the short term. Little evidence exists to guide subsequent rehabilitation actions to address this issue. Artificially re-introducing wood to such streams may be a useful strategy to increase habitat complexity more rapidly, thereby improving in-stream biodiversity values. To test this hypothesis, as a part of the larger Productive Grazing, Healthy Rivers project, small pieces of wood were introduced to eight sites on beef and dairy properties across southern Victoria, monitoring aquatic macroinvertebrates, water quality, hydrology and habitat quality. Comparing macroinvertebrate communities before and after treatment, and between experimental and control sites, changes in community composition and colonisation are explored.

Rehabilitating agricultural streams in Australia with wood : a review

Worldwide, the ecological condition of streams and rivers has been impaired by agricultural practices such as broadscale modification of catchments, high nutrient and sediment inputs, loss of riparian vegetation, and altered hydrology. Typical responses include channel incision, excessive sedimentation, declining water quality, and loss of in-stream habitat complexity and biodiversity. We review these impacts, focusing on the potential benefits and limitations of wood reintroduction as a transitional rehabilitation technique in these agricultural landscapes using Australian examples. In streams, wood plays key roles in shaping velocity and sedimentation profiles, forming pools, and strengthening banks. In the simplified channels typical of many agricultural streams, wood provides habitat for fauna, substrate for biofilms, and refuge from predators and flow extremes, and enhances in-stream diversity of fish and macroinvertebrates.

Most previous restoration studies involving wood reintroduction have been in forested landscapes, but some results might be extrapolated to agricultural streams. In these studies, wood enhanced diversity of fish and macroinvertebrates, increased storage of organic material and sediment, and improved bed and bank stability. Failure to meet restoration objectives appeared most likely where channel incision was severe and in highly degraded environments. Methods for wood reintroduction have logistical advantages over many other restoration techniques, being relatively low cost and low maintenance. Wood reintroduction is a viable transitional restoration technique for agricultural landscapes likely to rapidly improve stream condition if sources of colonists are viable and water quality is suitable.

Does adding wood to agricultural streams enhance biodiversity? An experimental approach

Riparian clearing and the removal of wood from channels have affected many streams in agricultural landscapes. As a result, these streams often have depauperate in-stream wood loads, and therefore decreased habitat complexity and lower levels of in-stream biodiversity. The introduction of wood was investigated as a possible rehabilitation technique for agricultural streams. Wood was re-introduced to eight streams in two separate high-rainfall, intensively grazed regions of Victoria, Australia and the effect on aquatic macroinvertebrate communities was measured. The addition of wood increased overall family richness and the richness of most functional feeding groups occupying edge and benthic habitats within the stream. Wood addition led to less overlap between benthic and edge macroinvertebrate communities, suggesting increased habitat heterogeneity within the stream ecosystem. Of all sampled habitats, wood supported the greatest density of families and was colonised by all functional feeding groups. Wood habitats also had the highest overall richness and supported the most taxa that were sensitive to disturbance. These findings suggest that re-introducing wood to agricultural streams is an appropriate rehabilitation technique where those streams are affected by reduced habitat complexity. Additional work is needed to confirm these findings over larger spatial and temporal scales.

Large versus small wood in streams : the effect of wood dimension on macroinvertebrate communities

Conventionally, most research and restoration involving in-stream wood focuses on large wood (>0.1 m diameter), excluding any smaller pieces. However, this may neglect a major component of in-stream habitat, as small wood can constitute the majority of pieces, particularly in small streams. The ecological benefit of large wood is well established, but corresponding benefits associated with small wood (0.05-0.1 m diameter) have not been demonstrated. To test the effect of wood dimension on macroinvertebrate community composition, we compared the fauna occupying large wood habitats with that occupying small wood at eight streams in south-eastern Australia. The relationships between wood dimensions and its macroinvertebrate fauna were complex. Community composition did not vary with wood dimension, and no significant correlations were found between other macroinvertebrate attributes (including family richness and evenness) and wood dimension, including diameter. However, analysis of covariance suggested that large wood supported a greater diversity and abundance of macroinvertebrates, indicating that the method of analysis could influence the result. Adjustment for differences in sample dimension using rarefaction determined that these findings were likely to be a result of the surface area and volumes sampled varying with the dimension of the wood. Per unit surface area, and per unit volume, small wood supported a similar number of families to large wood. Thus we conclude that, relative to the available surface area, small and large wood can be equivalent in their contribution to the available habitat in a stream. Therefore, the potential value of small wood as a habitat resource warrants its explicit consideration for inclusion in ecological and rehabilitation studies.

Main streams and fine rivers : thinking the aporetics of edge

This paper takes up the question (reframed by Deleuze and Guattari) of where expansion takes place: at the ends or from the centre. Despite the connotations of mediocrity that can be attributed to the term ‘mainstream’, it is possible to rethink what happens at close range as the space of radical openings. Writers can often believe that what is most abnormal or fringe will produce the highest probability of creative ‘event’. The question, however, can be posed – framed by the lineage of deconstruction – whether the key to unlocking any system of totality or closed possibility may lie in a very central (although physically peripheral) location. If, instead of the classical image, expansion may occur from re-imagined ‘middles’ rather than conventional ‘margins’, this reading of where potential can arise may offer a more resilient model than that of fragile peripheries, forever exposed to being amputated from staid centres of status and restricted participation. Drawing on the writings of Deleuze and Guattari, Derrida and Badiou, this paper seeks to unsettle any simplistic approach to the notion of edge, reinscribing it within the repetitiveness of our situations, to argue that right in the middle of the so-called mainstream, there might be the fine rivers of aporia that when encountered in thought can constitutes gates to that which is most radical in writing and other creative practices.

Real-time detection of parked vehicles from multiple image streams

We present a system to detect parked vehicles in a typical commercial parking complex using multiple streams of images captured through IP connected devices. Compared to traditional object detection techniques and machine learning methods, our approach is significantly faster in detection speed in the presence of multiple image streams. It is also capable of comparable accuracy when put to test against existing methods. And this is achieved without the need to train the system that machine learning methods require. Our approach uses a combination of psychological insights obtained from human detection and an algorithm replicating the outcomes of a SVM learner but without the noise that compromises accuracy in the normal learning process. The result is faster detection with comparable accuracy. Our experiments on images captured from a local test site shows very promising results for an implementation that is not only effective and low cost but also opens doors to new parking applications when combined with other technologies.

Highly efficient serial episode discovery in streams and complex sequences

This research proposed a series of methodologies and algorithms for highly efficient serial episode discovery in streams and complex sequences, and applied the developed techniques to quantitative analysis of the effects of price promotions. This research has outputted nine ERA ranking AlB papers published in international journals and conferences.

An approach for removing redundant data from RFID data streams

Radio frequency identification (RFID) systems are emerging as the primary object identification mechanism, especially in supply chain management. However, RFID naturally generates a large amount of duplicate readings. Removing these duplicates from the RFID data stream is paramount as it does not contribute new information to the system and wastes system resources. Existing approaches to deal with this problem cannot fulfill the real time demands to process the massive RFID data stream. We propose a data filtering approach that efficiently detects and removes duplicate readings from RFID data streams. Experimental results show that the proposed approach offers a significant improvement as compared to the existing approaches.

Effective anomaly detection in sensor networks data streams

This paper addresses a major challenge in data mining applications where the full information about the underlying processes, such as sensor networks or large online database, cannot be practically obtained due to physical limitations such as low bandwidth or memory, storage, or computing power. Motivated by the recent theory on direct information sampling called compressed sensing (CS), we propose a framework for detecting anomalies from these largescale data mining applications where the full information is not practically possible to obtain. Exploiting the fact that the intrinsic dimension of the data in these applications are typically small relative to the raw dimension and the fact that compressed sensing is capable of capturing most information with few measurements, our work show that spectral methods that used for volume anomaly detection can be directly applied to the CS data with guarantee on performance. Our theoretical contributions are supported by extensive experimental results on large datasets which show satisfactory performance.

Infrequent item mining in multiple data streams

The problem of extracting infrequent patterns from streams and building associations between these patterns is becoming increasingly relevant today as many events of interest such as attacks in network data or unusual stories in news data occur rarely. The complexity of the problem is compounded when a system is required to deal with data from multiple streams. To address these problems, we present a framework that combines the time based association mining with a pyramidal structure that allows a rolling analysis of the stream and maintains a synopsis of the data without requiring increasing memory resources. We apply the algorithms and show the usefulness of the techniques. © 2007 Crown Copyright.

An efficient one-pass method for discovering bases of recently frequent episodes over online data streams

The knowledge embedded in an online data stream is likely to change over time due to the dynamic evolution of the stream. Consequently, infrequent episode mining over an online stream, frequent episodes should be adaptively extracted from recently generated stream segments instead of the whole stream. However, almost all existing frequent episode mining approaches find episodes frequently occurring over the whole sequence. This paper proposes and investigates a new problem: online mining of recently frequent episodes over data streams. In order to meet strict requirements of stream mining such as one-scan, adaptive result update and instant result return, we choose a novel frequency metric and define a highly condensed set called the base of recently frequent episodes. We then introduce a one-pass method for mining bases of recently frequent episodes. Experimental results show that the proposed method is capable of finding bases of recently frequent episodes quickly and adaptively. The proposed method outperforms the previous approaches with the advantages of one-pass, instant result update and return, more condensed resulting sets and less space usage.