WebPut : Efficient web-based data imputation

In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is also proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Experiments based on several real-world data collections demonstrate that WebPut outperforms existing approaches.

A web-based approach to data imputation

In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Moreover, several optimization techniques are also proposed to reduce the cost of estimating the confidence of imputation queries at both the tuple-level and the database-level. Experiments based on several real-world data collections demonstrate not only the effectiveness of WebPut compared to existing approaches, but also the efficiency of our proposed algorithms and optimization techniques.

‘Structuring’ judicial sentencing discretion : consistency, guidance or pandering to the punitive?

Until quite recently, most Australian jurisdictions gave statutory force to the principle of imprisonment as a sanction of last resort, reflecting its status as the most punitive sentencing option open to the court.1 That principle gave primary discretion as to whether incarceration was the most appropriate means of achieving the purpose of a sentence to the sentencing court, which received all of the information relevant to the offence, the offender and any victim(s). The disestablishment of this principle is symptomatic of an increasing erosion of judicial discretion with respect to sentencing, which appears to be resulting in some extremely punitive consequences.

Using old concepts to gain new insights: Addressing the issue of consistency

Purpose – This paper aims to go beyond a bookkeeping approach to evolutionary analysis whereby surviving firms are better adapted and extinct firms were less adapted. From discussion of the preliminary findings of research into the Hobart pizza industry, evidence is presented of the need to adopt a more traditional approach to applying evolutionary theories with organizational research. Design/methodology/approach – After a brief review of the relevant literature, the preliminary findings of research into the Hobart pizza industry are presented. Then, several evolutionary concepts that are commonplace in ecological research are introduced to help explain the emergent findings. The paper concludes with consideration given to advancing a more consistent approach to employing evolutionary theories within organizational research. Findings – The paper finds that the process of selection cannot be assumed to occur evenly across time and/or space. Within geographically small markets different forms of selection operate in different ways and degrees requiring the use of more traditional evolutionary theories to highlight the causal process associated with population change. Research limitations/implications – The paper concludes by highlighting Geoffrey Hodgson’s Principle of Consistency. It is demonstrated that a failure to truly understand how and why theory is used in one domain will likely result in its misuse in another domain. That, at present, too few evolutionary concepts are employed in organisational research to ensure an appreciation of any underlying causal processes through which social change occurs. Originality/value – The concepts introduced throughout this paper, whilst not new, provide new entry points for organizational researchers intent on employing an evolutionary approach to understand the process of social change.

Property cash flow studies : focusing on model consistency and data accuracy

The high degree of variability and inconsistency in cash flow study usage by property professionals demands improvement in knowledge and processes. Until recently limited research was being undertaken on the use of cash flow studies in property valuations but the growing acceptance of this approach for major investment valuations has resulted in renewed interest in this topic. Studies on valuation variations identify data accuracy, model consistency and bias as major concerns. In cash flow studies there are practical problems with the input data and the consistency of the models. This study will refer to the recent literature and identify the major factors in model inconsistency and data selection. A detailed case study will be used to examine the effects of changes in structure and inputs. The key variable inputs will be identified and proposals developed to improve the selection process for these key variables. The variables will be selected with the aid of sensitivity studies and alternative ways of quantifying the key variables explained. The paper recommends, with reservations, the use of probability profiles of the variables and the incorporation of this data in simulation exercises. The use of Monte Carlo simulation is demonstrated and the factors influencing the structure of the probability distributions of the key variables are outline. This study relates to ongoing research into functional performance of commercial property within an Australian Cooperative Research Centre.