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Remote sensing technology in recent years has been regarded the most important source to provide substantial information for delineating the flooding extent to the disaster management authority. There have been numerous studies proposing mathematical or statistical classification models for flood mapping. However, conventional pixel-wise classifications methods rely on the exact match of the spectral signature to label the target pixel. In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. We utilised the spatial information from the neighbouring area of target pixel in classification. A total of 64 different models were generated and trained with a variable neighbourhood size of training samples and number of learnable filters. The training results revealed that the model trained with 3 × 3 neighbourhood sized training samples and with 32 convolutional filters achieved the best performance out of the experiments. A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. A comparison of our proposed classification model to the conventional support vector machines (SVM) classification model shows that the F-CNNs model was able to detect flooded areas more efficiently than the SVM classification model. For example, the F-CNNs model achieved a maximum precision rate (true positives) of 76.7% compared to 45.27% for SVM classification.
Chandrama Sarker; Luis Mejias; Frederic Maire; Alan Woodley. Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information. Remote Sensing 2019, 11, 2331 .
AMA StyleChandrama Sarker, Luis Mejias, Frederic Maire, Alan Woodley. Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information. Remote Sensing. 2019; 11 (19):2331.
Chicago/Turabian StyleChandrama Sarker; Luis Mejias; Frederic Maire; Alan Woodley. 2019. "Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information." Remote Sensing 11, no. 19: 2331.
Satellite images are capable of providing valuable, synoptic coverage of the environment and so have been used for natural disaster assessment such as flooding. There are plenty of machine learning classifiers that can detect water in satellite images and although none are perfect they often produce acceptable results. Ensemble classifiers combine multiple classifiers and are often able to outperform their constitute classifiers. Ensemble classifiers are known to be effective for image classification in different applications but are unexplored for water detection in satellite images. This research employs an ensemble classifier to detect water in satellite images for flood assessment. Classification was performed both using individual bands and Normalized Difference Water Index (NDWI). The results show that to improve the classification accuracy with ensemble classifiers it is important to choose appropriate classifiers to ensemble. It also shows that this approach is capable of producing good classification accuracy for a seen location when bands are used and an unseen location when NDWI is used.
Rabiul Islam Jony; Alan Woodley; Aishvarya Raj; Dimitri Perrin. Ensemble Classification Technique for Water Detection in Satellite Images. 2018 Digital Image Computing: Techniques and Applications (DICTA) 2018, 1 -8.
AMA StyleRabiul Islam Jony, Alan Woodley, Aishvarya Raj, Dimitri Perrin. Ensemble Classification Technique for Water Detection in Satellite Images. 2018 Digital Image Computing: Techniques and Applications (DICTA). 2018; ():1-8.
Chicago/Turabian StyleRabiul Islam Jony; Alan Woodley; Aishvarya Raj; Dimitri Perrin. 2018. "Ensemble Classification Technique for Water Detection in Satellite Images." 2018 Digital Image Computing: Techniques and Applications (DICTA) , no. : 1-8.
At times, remote sensing images do not capture every pixel in a target location due to atmospheric or electronic disturbance. These uncaptured pixels are referred to as missing pixels. Since missing pixels may contain important data, they can negatively affect downstream applications and scientific fields. A number of methods have been developed to predict the value of missing pixels and can be classified into four groups: deterministic, geostatistic, auxiliary image-based and hybrid methods. Each method has limitations with a common limitation being predicting change for a particular environmental class. Here, we introduce the Integration of Geostatistic and Temporal Missing Pixels' Properties (IGTMPP) as a hybrid method to predict missing pixels. IGTMPP combines geostatistical and temporal relationships between pixels, based on how similar pixels to a target pixel changed over time. IGTMPP was tested using Landsat 5 images of different areas in Queensland, Australia and compared with three baseline methods. The results showed that IGTMPP outperformed the baseline methods.
Thaer F. Ali; Alan Woodley. IGTMPP: A Hybrid Method to Predict Missing Pixels of Remote Sensing Images using Geo-Temporal Properties. 2018 Digital Image Computing: Techniques and Applications (DICTA) 2018, 1 -8.
AMA StyleThaer F. Ali, Alan Woodley. IGTMPP: A Hybrid Method to Predict Missing Pixels of Remote Sensing Images using Geo-Temporal Properties. 2018 Digital Image Computing: Techniques and Applications (DICTA). 2018; ():1-8.
Chicago/Turabian StyleThaer F. Ali; Alan Woodley. 2018. "IGTMPP: A Hybrid Method to Predict Missing Pixels of Remote Sensing Images using Geo-Temporal Properties." 2018 Digital Image Computing: Techniques and Applications (DICTA) , no. : 1-8.
Clustering is a popular technique that can help make large datasets more manageable and usable by grouping together similar objects. Most clustering approaches are too computationally expensive for datasets that are very large or complex. Here, we present Parallel K-Tree, a hierarchical data structure and clustering algorithm that takes advantage of modern computing environments to cluster extremely large data sets. We show that Parallel K-Tree produces high-quality clusters and scales more efficiently than traditional, parallelized and state-of-the-art approaches. Finally, we discuss how we applied Parallel K-Tree to a large (8 terabyte) collection of Landsat 5 satellite images. This required clustering of 540 billion objects into eight billion clusters - a two orders of magnitude size increase over any reported alternative approach. Furthermore, Parallel K-Tree was executed on just two commodity servers - rather than a high-performance supercomputer.
Alan Woodley; Ling-Xiang Tang; Shlomo Geva; Richi Nayak; Timothy Chappell. Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems 2018, 99, 333 -345.
AMA StyleAlan Woodley, Ling-Xiang Tang, Shlomo Geva, Richi Nayak, Timothy Chappell. Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems. 2018; 99 ():333-345.
Chicago/Turabian StyleAlan Woodley; Ling-Xiang Tang; Shlomo Geva; Richi Nayak; Timothy Chappell. 2018. "Parallel K-Tree: A multicore, multinode solution to extreme clustering." Future Generation Computer Systems 99, no. : 333-345.
Data aggregation is a necessity when working with big data. Data reduction steps without loss of information are a scientific and computational challenge but are critical to enable effective data processing and information delineation in data-rich studies. We investigated the effect of four spatial aggregation schemes on Landsat imagery on prediction accuracy of green photosynthetic vegetation (PV) based on fractional cover (FCover). To reduce data volume we created an evenly spaced grid, overlaid that on the PV band and delineated the arithmetic mean of PV fractions contained within each grid cell. The aggregated fractions and the corresponding geographic grid cell coordinates were then used for boosted regression tree prediction models. Model goodness of fit was evaluated by the Root Mean Squared Error (RMSE). Two spatial resolutions (3000 m and 6000 m) offer good prediction accuracy whereas others show either too much unexplained variability model prediction results or the aggregation resolution smoothed out local PV in heterogeneous land. We further demonstrate the suitability of our aggregation scheme, offering an increased processing time without losing significant topographic information. These findings support the feasibility of using geographic coordinates in the prediction of PV and yield satisfying accuracy in our study area.
Brigitte Colin; Michael Schmidt; Samuel Clifford; Alan Woodley; Kerrie Mengersen. Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees. Remote Sensing 2018, 10, 1260 .
AMA StyleBrigitte Colin, Michael Schmidt, Samuel Clifford, Alan Woodley, Kerrie Mengersen. Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees. Remote Sensing. 2018; 10 (8):1260.
Chicago/Turabian StyleBrigitte Colin; Michael Schmidt; Samuel Clifford; Alan Woodley; Kerrie Mengersen. 2018. "Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees." Remote Sensing 10, no. 8: 1260.
Remote sensing data is becoming readily available on the Web. However, remote sensing data is not as widely used as other web data. Here, we explore how a fusion of remote sensing data with web paradigms and data would benefit the remote sensing community. We present a framework to achieve this fusion which extends on previous research by: 1) being generic, so that it can be used in any situation; 2) automatic, so that it requires almost no input from the user, and 3) archival, allowing access to decades of remote sensing and web information. We demonstrate our framework with a proof-of-concept prototype.
Alan Woodley; Timothy Chappell; Shlomo Geva; Richi Nayak. Using web services to fuse remote sensing and multimedia data repositories. Proceedings of the Australasian Computer Science Week Multiconference 2017, 54 -54:8.
AMA StyleAlan Woodley, Timothy Chappell, Shlomo Geva, Richi Nayak. Using web services to fuse remote sensing and multimedia data repositories. Proceedings of the Australasian Computer Science Week Multiconference. 2017; ():54-54:8.
Chicago/Turabian StyleAlan Woodley; Timothy Chappell; Shlomo Geva; Richi Nayak. 2017. "Using web services to fuse remote sensing and multimedia data repositories." Proceedings of the Australasian Computer Science Week Multiconference , no. : 54-54:8.
This paper addresses the challenge of introducing a Bayes rules to measure flood probability from multispectral data. Machine learning classifiers were applied to map the extent of flood inun- dation from multispectral remote sensing imagery. The paper applies Extended Support Vector Machine classifier along with linear spectral un-mixing to obtain the classification output. K-means clustering is applied on pre and post flood images to select SVM training samples from clustering outcome of the most in-formative spectral band. Experiments were conducted by divid-ing training and testing samples into two groups. The efficiency of classifier was enhanced by introducing the Bayesian probabil-ity measure and performance was assessed by using precision and recall metrics on the pre and post Bayesian flood probability estimation. It has been observed that for some test cases in this study there was a substantial improvement in precision-recall curve with high precision values and low recall rate. The optimal flood probability threshold value has also been easily calculated by calculating and analising iteratively precision and recall.
Chandrama Sarker; Luis Mejias Alvarez; Alan Woodley. Integrating Recursive Bayesian Estimation with Support Vector Machine to Map Probability of Flooding from Multispectral Landsat Data. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2016, 1 -8.
AMA StyleChandrama Sarker, Luis Mejias Alvarez, Alan Woodley. Integrating Recursive Bayesian Estimation with Support Vector Machine to Map Probability of Flooding from Multispectral Landsat Data. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). 2016; ():1-8.
Chicago/Turabian StyleChandrama Sarker; Luis Mejias Alvarez; Alan Woodley. 2016. "Integrating Recursive Bayesian Estimation with Support Vector Machine to Map Probability of Flooding from Multispectral Landsat Data." 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) , no. : 1-8.
Clustering can help to make large datasets more manageable by grouping together similar objects. However, most clustering approaches are unable to scale to very large datasets (e.g. more than 10 million objects). The K-Tree is a data structure and clustering algorithm that has proven to be scalable with large streaming datasets. Here, we apply the K-Tree to spatial data (satellite images) and extend from a single threaded to a multicore environment. We show that the K-Tree is able to cluster larger dataset more efficiently than baseline approaches.
Alan Woodley; Ling-Xiang Tang; Shlomo Geva; Richi Nayak; Timothy Chappell. Using parallel hierarchical clustering to address spatial big data challenges. 2016 IEEE International Conference on Big Data (Big Data) 2016, 2692 -2698.
AMA StyleAlan Woodley, Ling-Xiang Tang, Shlomo Geva, Richi Nayak, Timothy Chappell. Using parallel hierarchical clustering to address spatial big data challenges. 2016 IEEE International Conference on Big Data (Big Data). 2016; ():2692-2698.
Chicago/Turabian StyleAlan Woodley; Ling-Xiang Tang; Shlomo Geva; Richi Nayak; Timothy Chappell. 2016. "Using parallel hierarchical clustering to address spatial big data challenges." 2016 IEEE International Conference on Big Data (Big Data) , no. : 2692-2698.
Hyperspectral images typically contain hundreds of spectral bands which is one to two orders of magnitude larger than the number of bands in multispectral images. This greater volume of spectral information could lead to discoveries that are not possible with multispectral images; however, overcoming the complexity of the additional information is a computational challenge. Here, we present a solution that uses feature selection, logarithmic nearest neighbor classification and neighborhood spatial analysis to classify the land use of multiple hyperspectral images. Empirical analysis shows that our solution is as accurate as other much more complex approaches and it is orders-of-magnitude more efficient. This ascertains that our solution is scalable to larger datasets while maintaining high accuracy.
Alan Woodley; Timothy Chappell; Shlomo Geva; Richi Nayak. Efficient Feature Selection and Nearest Neighbour Search for Hyperspectral Image Classification. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2016, 1 -8.
AMA StyleAlan Woodley, Timothy Chappell, Shlomo Geva, Richi Nayak. Efficient Feature Selection and Nearest Neighbour Search for Hyperspectral Image Classification. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA). 2016; ():1-8.
Chicago/Turabian StyleAlan Woodley; Timothy Chappell; Shlomo Geva; Richi Nayak. 2016. "Efficient Feature Selection and Nearest Neighbour Search for Hyperspectral Image Classification." 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) , no. : 1-8.
Location-aware social media is increasing being used to inform decisions in a spatiotemporal context. However, collecting, fusing, processing and merging information from different social media platforms is a challenge because of diversity of information between different platforms. Here, we present the WIMBY, which is able to access multiple social media platforms to help users answer the question \"What's in my Backyard?\". In doing so, the WIMBY helps to address the challenge of dealing with diverse social media information. It is believed that the WIMBY can be extended to include more information sources (including other social media platforms) to help inform decision makers in a wider array of applications.
Michael Dorkhom; Alan Woodley; Shlomo Geva; Richi Nayak. WIMBY. Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics 2016, 714 -716.
AMA StyleMichael Dorkhom, Alan Woodley, Shlomo Geva, Richi Nayak. WIMBY. Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics. 2016; ():714-716.
Chicago/Turabian StyleMichael Dorkhom; Alan Woodley; Shlomo Geva; Richi Nayak. 2016. "WIMBY." Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics , no. : 714-716.
The Social Water Assessment Protocol (SWAP) is a tool consisting of a series of questions on 14 themes designed to capture the social context of water around a mine site. A pilot study of the SWAP, conducted in Prestea-Huni Valley, Ghana, showed that some communities were concerned about whether the groundwater was potable. The mining company’s concern was that there was a cycle of dependency amongst communities that received treated water from the mining company. The pilot identified potential data sources and stakeholder groups for each theme, and gaps in themes, and suggested refinements to questions to improve the SWAP.
Anastasia N. Danoucaras; Alidu Babatu Adam; Kathryn Sturman; Nina K. Collins; Alan Woodley. A pilot study of the Social Water Assessment Protocol in a mining region of Ghana. Water International 2016, 41, 1 -17.
AMA StyleAnastasia N. Danoucaras, Alidu Babatu Adam, Kathryn Sturman, Nina K. Collins, Alan Woodley. A pilot study of the Social Water Assessment Protocol in a mining region of Ghana. Water International. 2016; 41 (3):1-17.
Chicago/Turabian StyleAnastasia N. Danoucaras; Alidu Babatu Adam; Kathryn Sturman; Nina K. Collins; Alan Woodley. 2016. "A pilot study of the Social Water Assessment Protocol in a mining region of Ghana." Water International 41, no. 3: 1-17.
Many developing countries are experiencing rapid expansion in mining with associated water impacts. In most cases mining expansion is outpacing the building of national capacity to ensure that sustainable water management practices are implemented. Since 2011, Australia's International Mining for Development Centre (IM4DC) has funded capacity building in such countries including a program of water projects. Five projects in particular (principally covering experiences from Peru, Colombia, Ghana, Zambia, Indonesia, Philippines and Mongolia) have provided insight into water capacity building priorities and opportunities. This paper reviews the challenges faced by water stakeholders, and proposes the associated capacity needs. The paper uses the evidence derived from the IM4DC projects to develop a set of specific capacity-building recommendations. Recommendations include: the incorporation of mine water management in engineering and environmental undergraduate courses; secondments of staff to suitable partner organisations; training to allow site staff to effectively monitor water including community impacts; leadership training to support a water stewardship culture; training of officials to support implementation of catchment management approaches; and the empowerment of communities to recognise and negotiate solutions to mine-related risks. New initiatives to fund the transfer of multi-disciplinary knowledge from nations with well-developed water management practices are called for.
Neil McIntyre; Alan Woodley; Anastasia Danoucaras; Neil Coles. Water management capacity building to support rapidly developing mining economies. Water Policy 2015, 17, 1191 -1208.
AMA StyleNeil McIntyre, Alan Woodley, Anastasia Danoucaras, Neil Coles. Water management capacity building to support rapidly developing mining economies. Water Policy. 2015; 17 (6):1191-1208.
Chicago/Turabian StyleNeil McIntyre; Alan Woodley; Anastasia Danoucaras; Neil Coles. 2015. "Water management capacity building to support rapidly developing mining economies." Water Policy 17, no. 6: 1191-1208.
The mining industry has positioned itself within the sustainability agenda, particularly since the establishment of the International Council of Mining and Minerals (ICMM). However, some critics have questioned this position, since mining requires the extraction of non-renewable finite resources and commercial mining companies have the specific responsibility to produce profit. Complicating matters is that terms that represent the sustainability such as ‘sustainability’ and ‘sustainable development’ have multiple definitions with varying degrees of sophistication. This work identifies eleven sustainability agenda definitions that are applicable to the mining industry and organises them into three tiers: first, Perpetual Sustainability, that focuses on mining continuing indefinitely with its benefits limited to immediate shareholders; second, Transferable Sustainability, that focuses on how mining can benefit society and the environment and third, Transitional Sustainability, that focuses on the intergenerational benefits to society and the environment even after mining ceases. Using these definitions, a discourse analysis was performed on sustainability reports from member companies of the ICMM and the academic journal Resources Policy. The discourse analysis showed that in both media the definition of the sustainability agenda was focussed on Transferable Sustainability, with the sustainability reports focused on how it can be applied within a business context while the academic journal took a broader view of mining’s social and environmental impacts
A. Han Onn; Alan Woodley. A discourse analysis on how the sustainability agenda is defined within the mining industry. Journal of Cleaner Production 2014, 84, 116 -127.
AMA StyleA. Han Onn, Alan Woodley. A discourse analysis on how the sustainability agenda is defined within the mining industry. Journal of Cleaner Production. 2014; 84 ():116-127.
Chicago/Turabian StyleA. Han Onn; Alan Woodley. 2014. "A discourse analysis on how the sustainability agenda is defined within the mining industry." Journal of Cleaner Production 84, no. : 116-127.
Communicating the mining industry’s water use is fundamental to maintaining its social license to operate but the majority of corporate reporting schemes list indicators. The Minerals Council of Australia’s Water Accounting Framework was designed to assist the minerals industry obtain consistency in its accounting method and in the definitions of terms used in water reporting. The significance of this paper is that it shows that the framework has been designed to be sufficiently robust to describe any mining/mineral related operation. The Water Accounting Framework was applied across four operations over three countries producing four commodities. The advantages of the framework were then evident through the presentation of the reports. The contextual statement of the framework was able to explain contrasting reuse efficiencies. The Input-Output statements showed that evaporation was a significant loss for most of the operations in the study which highlights a weakness of reporting schemes that focus on discharge volumes. The framework method promotes data reconciliation which proved the presence of flows that two operations in the study had neglected to provide. Whilst there are many advantages of the framework, the major points are that the reporting statements of the framework, when presented together, can better enable the public to understand water interactions at a site-level and allows for valid comparisons between sites, regardless of locale and commodity. With mining being a global industry, these advantages are best realised if there was international adoption of the framework
A.N. Danoucaras; A.P. Woodley; C.J. Moran. The robustness of mine water accounting over a range of operating contexts and commodities. Journal of Cleaner Production 2014, 84, 727 -735.
AMA StyleA.N. Danoucaras, A.P. Woodley, C.J. Moran. The robustness of mine water accounting over a range of operating contexts and commodities. Journal of Cleaner Production. 2014; 84 ():727-735.
Chicago/Turabian StyleA.N. Danoucaras; A.P. Woodley; C.J. Moran. 2014. "The robustness of mine water accounting over a range of operating contexts and commodities." Journal of Cleaner Production 84, no. : 727-735.
Nigel Paragreen; Alan Woodley. Social licence to operate and the coal seam gas industry: What can be learnt from already established mining operations? Rural Society 2013, 23, 46 -59.
AMA StyleNigel Paragreen, Alan Woodley. Social licence to operate and the coal seam gas industry: What can be learnt from already established mining operations? Rural Society. 2013; 23 (1):46-59.
Chicago/Turabian StyleNigel Paragreen; Alan Woodley. 2013. "Social licence to operate and the coal seam gas industry: What can be learnt from already established mining operations?" Rural Society 23, no. 1: 46-59.
The human right to water has recently been recognized by both the United Nations General Assembly and the Human Rights Council. As the mining industry interacts with water on multiple levels, it is important that these interactions respect the human right to water. Currently, a disconnect exists between mine site water management practices and the recognition of water from a human rights perspective. It has been argued that the Minerals Council of Australia Water Accounting Framework can be used to strengthen the connection between water management and human rights. This article extends this connection through the use of a Social Water Assessment Protocol (SWAP). The SWAP is a scoping tool consisting of a set of questions classified into taxonomic themes under leading topics with suggested sources of data that enable mine sites to better understand the local water context in which they operate. Three of the themes contained in the SWAP – gender, Indigenous peoples and health – are discussed to demonstrate how the protocol may be useful in assisting mining companies to consider their impacts on the human right to water.
Nina Collins; Alan Woodley. Social water assessment protocol: a step towards connecting mining, water and human rights. Impact Assessment and Project Appraisal 2013, 31, 158 -167.
AMA StyleNina Collins, Alan Woodley. Social water assessment protocol: a step towards connecting mining, water and human rights. Impact Assessment and Project Appraisal. 2013; 31 (2):158-167.
Chicago/Turabian StyleNina Collins; Alan Woodley. 2013. "Social water assessment protocol: a step towards connecting mining, water and human rights." Impact Assessment and Project Appraisal 31, no. 2: 158-167.
INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2008 evaluation campaign, which consisted of a wide range of tracks: Ad hoc, Book, Efficiency, Entity Ranking, Interactive, QA, Link the Wiki, and XML Mining.
Gianluca DeMartin; Ludovic Denoye; Antoine Douce; Khairun Nisa Fachry; Patrick Gallinar; Shlomo Gev; Wei-Che Huang; Tereza Iofciu; Jaap Kamps; Gabriella Kazai; Marijn Koolen; Monica Landoni; Ragnar Nordlie; Nils Pharo; Ralf Schenkel; Martin Theobald; Andrew Trotman; Arjen P. De Vries; Alan Woodley; Jianhan Zhu. Report on INEX 2008. ACM SIGIR Forum 2009, 43, 17 -36.
AMA StyleGianluca DeMartin, Ludovic Denoye, Antoine Douce, Khairun Nisa Fachry, Patrick Gallinar, Shlomo Gev, Wei-Che Huang, Tereza Iofciu, Jaap Kamps, Gabriella Kazai, Marijn Koolen, Monica Landoni, Ragnar Nordlie, Nils Pharo, Ralf Schenkel, Martin Theobald, Andrew Trotman, Arjen P. De Vries, Alan Woodley, Jianhan Zhu. Report on INEX 2008. ACM SIGIR Forum. 2009; 43 (1):17-36.
Chicago/Turabian StyleGianluca DeMartin; Ludovic Denoye; Antoine Douce; Khairun Nisa Fachry; Patrick Gallinar; Shlomo Gev; Wei-Che Huang; Tereza Iofciu; Jaap Kamps; Gabriella Kazai; Marijn Koolen; Monica Landoni; Ragnar Nordlie; Nils Pharo; Ralf Schenkel; Martin Theobald; Andrew Trotman; Arjen P. De Vries; Alan Woodley; Jianhan Zhu. 2009. "Report on INEX 2008." ACM SIGIR Forum 43, no. 1: 17-36.
This paper gives an overview of the INEX 2008 Ad Hoc Track. The main goals of the Ad Hoc Track were two-fold. The first goal was to investigate the value of the internal document structure (as provided by the XML mark-up) for retrieving relevant information. This is a continuation of INEX 2007 and, for this reason, the retrieval results are liberalized to arbitrary passages and measures were chosen to fairly compare systems retrieving elements, ranges of elements, and arbitrary passages. The second goal was to compare focused retrieval to article retrieval more directly than in earlier years. For this reason, standard document retrieval rankings have been derived from all runs, and evaluated with standard measures. In addition, a set of queries targeting Wikipedia have been derived from a proxy log, and the runs are also evaluated against the clicked Wikipedia pages. The INEX 2008 Ad Hoc Track featured three tasks: For the Focused Task a ranked-list of non-overlapping results (elements or passages) was needed. For the Relevant in Context Task non-overlapping results (elements or passages) were returned grouped by the article from which they came. For the Best in Context Task a single starting point (element start tag or passage start) for each article was needed. We discuss the results for the three tasks, and examine the relative effectiveness of element and passage retrieval. This is examined in the context of content only (CO, or Keyword) search as well as content and structure (CAS, or structured) search. Finally, we look at the ability of focused retrieval techniques to rank articles, using standard document retrieval techniques, both against the judged topics as well as against queries and clicks from a proxy log.
Jaap Kamps; Shlomo Geva; Andrew Trotman; Alan Woodley; Marijn Koolen. Overview of the INEX 2008 Ad Hoc Track. Transactions on Petri Nets and Other Models of Concurrency XV 2009, 1 -28.
AMA StyleJaap Kamps, Shlomo Geva, Andrew Trotman, Alan Woodley, Marijn Koolen. Overview of the INEX 2008 Ad Hoc Track. Transactions on Petri Nets and Other Models of Concurrency XV. 2009; ():1-28.
Chicago/Turabian StyleJaap Kamps; Shlomo Geva; Andrew Trotman; Alan Woodley; Marijn Koolen. 2009. "Overview of the INEX 2008 Ad Hoc Track." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 1-28.
Alan Woodley; Shlomo Geva; Sylvia L. Edwards. What XML-IR Users May Want. Computer Vision 2007, 423 -431.
AMA StyleAlan Woodley, Shlomo Geva, Sylvia L. Edwards. What XML-IR Users May Want. Computer Vision. 2007; ():423-431.
Chicago/Turabian StyleAlan Woodley; Shlomo Geva; Sylvia L. Edwards. 2007. "What XML-IR Users May Want." Computer Vision , no. : 423-431.
XML information retrieval (XML-IR) systems aim to provide users with highly exhaustive and highly specific results. To interact with XML-IR systems users must express both their content and structural needs in the form of a structured query. Historically, these structured queries have been formatted using formal languages such as XPath or NEXI. Unfortunately, formal query languages are very complex and too difficult to be used by experienced, let alone casual, users and are too closely bound to the underlying physical structure of the collection. Hence, recent research has investigated the idea of specifying users’ content and structural requirements via natural language queries (NLQs). The NLP track was established at INEX 2004 to promote research into this area, and QUT participated with the system NLPX. Here, we discuss changes we’ve made to the system since last year, as well as our participation in INEX 2005.
Alan Woodley; Shlomo Geva. NLPX at INEX 2005. Lecture Notes in Computer Science 2006, 358 -372.
AMA StyleAlan Woodley, Shlomo Geva. NLPX at INEX 2005. Lecture Notes in Computer Science. 2006; ():358-372.
Chicago/Turabian StyleAlan Woodley; Shlomo Geva. 2006. "NLPX at INEX 2005." Lecture Notes in Computer Science , no. : 358-372.