Although the existing research provides a framework, many utility specific heterogenei- ties remain to be resolved. For example, different units and reference systems are rea- sonably constrained as all companies use the Ordnance Survey National Grid projection. However, the Positional Accuracy Improvement (PAI - Ordnance Survey (2007)) pro- gramme, used to address accuracy issues in Ordnance Survey data that became apparent after the introduction of absolute positioning technologies (such as GPS), provides an 95% accuracy estimate of 1m in urban environments. The differences in precision and accuracy of relative and absolute positioning devices may increase data uncertainty. Furthermore, 3-dimensional representations of utility asset may be problematic. If the 3rd dimension is recorded, it is normally as a depth (a relative measure) or an Ordnance Survey height (an absolute measure). However, these fields are variably populated in every asset dataset. The challenge here is to identify the appropriate measurements and apply them to the 2-d polylines to create topologically correct 3-d polyline networks. Finally, though the literature is rich on techniques for resolving various heterogeneities, the assumption is that various meta-data and documentation is available to assist inte- gration work. Without good quality metadata some problems may be intractable.
created (for example, LiDAR-based data) for roadway inventory and asset management purposes, incorporating schedule (4D) and cost (5D) information into models, and using post-construction survey data to correct the design model and create an accurate as-built record drawing (including
“The HP UDC will allow us to flexibly adapt to the business fluctuations of the electronics industry. We’ll be able to reduce our total cost of ownership by streamlining data center management and reducing excess IT capacity while also incorporating the industry’s best platform for application consolidation.”
This empirical paper uses a form of proxy utilitydata to test theories of interdependent utility in marriage. Using a large UK panel sample from 11 waves of the BHPS data, we exploit the psychological nature of “residual” health status of each respondent’s spouse to provide an instrument for their current well-being at di¤erent time periods. Consistent with the standard assumptions in the economic and psychology literature, the IV estimates reveal a strong and positive correlation between own well-being and spouse’s well-being for the married sample in Great Britain. There is strong evidence that ‘happy’ individuals are likely to be observed living with a ‘happy’ partner, and this e¤ect is well-de…ned for most groups of married individuals in the data set. The results are also robust to controls for individual …xed e¤ects. That is, over-time changes in the respondent’s well-being are shown to be positively and signi…cantly correlated to over-time changes in the well-being of the respondent’s spouse. The estimated impacts of spouse’s well-being on the individual’s well-being are of a reasonable size. For instance, a change in the spouse’s well-being by one standard deviation can compensate around one third of the negative impact unemployment has on individual’s well-being.
Abstract: Cities around the world face an increasing need for land as density in urban areas increases rapidly. The pressure to expand a city’s space is especially acute for a city-state like Singapore. In the big data era, a data-driven approach of underground spaces is necessary for the sustainable development of a city along with rapid urbanization. A reliable three dimensional (3D) digital map of utility networks is crucial for urban planners to understand one of the most impactful aspects of underground space planning. How to map reliable 3D underground utility networks and use it in the land administration? This is a challenging issue, especially for cities with limited land resources, congested underground spaces, and a lack of uniform existing practices. First, this paper proposes a framework for utilitydata governance from the underground utilitydata survey to data usage. This is the backbone to support coordination of different roles in the utilitydata management and usage. Then, an initial design of the 3D utility cadastral data model is introduced, which aims to support the 3D modelling of utility networks and connect it to the cadastral parcel. It is expected that reliable and accurate information on underground utility networks can lead to a better understanding and management of underground space, which eventually contributes to better city planning, making the unseen structures visible.
Abstract: With the pressure of the increasing density of urban areas, some public infrastructures are moving to the underground to free up space above, such as utility lines, rail lines and roads. In the big data era, the three dimensional (3D) data can be beneficial to understand the complex urban area. Comparing to spatial data and information of the above ground, we lack of the precise and detailed information about underground infrastructures, such as the spatial information of underground infrastructure, the ownership of underground objects and the interdependence of infrastructures in the above and below ground. How to map reliable 3D underground utility networks and use it in the land administration? First, to explain the importance of this work and find a possible solution, this paper observes the current issues of the existing underground utility database in Singapore. A framework for utilitydata governance is proposed to manage the work process from the underground utilitydata capture to data usage. This is the backbone to support the coordination of different roles in the utilitydata governance and usage. Then, an initial design of the 3D underground utilitydata model is introduced to describe the 3D geometric and spatial information about underground utilitydata and connect it to the cadastral parcel for land administration. In the case study, the newly collected data from mobile Ground Penetrating Radar is integrated with the existing utilitydata for 3D modelling. It is expected to explore the integration of new collected 3D data, the existing 2D data and cadastral information for land administration of underground utilities.
The amount of data that need to be processed to extract some useful information is increasing. Therefore different data mining methods are adopted to get optimum result with respect to time and utility of data. The amount of personal data that can be collected and analyzed has also increased. Data mining tools are increasingly being used to infer trends and patterns. In many scenarios, access to large amounts of personal data is essential in order for accurate inferences to be drawn. However, publishing of data containing personal information has to be restricted so that individual privacy is not hampered. One possible solution is that instead of releasing the entire database, only a part of it is released which can answer the adequate queries and do not reveal sensitive information. Only those queries are answered which do not reveal sensitive information. Sometimes original data is perturbed and the database owner provides a perturbed answer to each query. These methods require the researchers to formulate their queries without access to any data. Sanitization approach can be used to anonymize the data in order to hide the exact values of the data. But conclusion can’t be drawn with surety. Another approach is to suppress some of the data values, while releasing the remaining data values exactly. But suppressing the data may hamper the utility. A lot of research work has been done to protect privacy and many models have been proposed to protect databases. Out of them, k-anonymity has received considerable attention from computer scientist. Under k-anonymity, each piece of disclosed data is equivalent to at least k-1 other pieces of disclosed data over a set of attributes that are deemed to be privacy sensitive.
Periodically, PJM need report model changes to their members & neighboring RTOs and have them confirm the accuracy of the model changes. This used to be primarily a manual process involving a lot of effort from both data providers and data recipients. The major challenges identified include:
CTU-Mine: Based on the T W U model, CTU-Mine  was proposed that is more efficient than Two-phase in dense databases when the minimum utility threshold is very low. In order to mine HUIs, a prefix tree, called CUP-tree, is constructed. It consists of an ItemT able called GlobalItemT able made up of all high T W U items and a tree called GlobalCUP-Tree with nodes linked by pointers from GlobalItemT able, contain- ing itemset information for utility mining. GlobalItemT able contains all individual HT W U items sorted in descending order by their T W U values. Then, a new algo- rithm that traverses the tree using a bottom-up approach is presented. During mining process, the algorithm constructs another tree called a High Utility Pattern Tree (HUP- Tree) to maintain high utility itemsets and their utility values computed by traversing the LocalCUP-Tree. This data structure and algorithm extend the pattern growth approach. Later, to reduce the number of candidates in each database scan, the isolated item dis- carding strategy (IIDS) was proposed in . Using IIDS , two efficient algorithms F U M and DCG+ were proposed by the authors.
Visual analytics addresses each of these challenges and completely changes the way ACOs can approach data. It can give these users the unique population health insights they need from data – such as performance measures, trends, costs, and outcomes– across multiple sites of care. Moreover, it provides these insights in a consumable format that can improve decision-making at the patient-level. This is not only a huge advancement for provider efficiency, but a game changer in making the ACO model successful. For example, the state of New Hampshire uses SAS® Visual Analytics in conjunction with the state’s all-payer claims database (APCD) to allow ACOs to dynamically view data and critical measures on their populations’ health. Through a web-based portal, ACOs in NH are able to use visualization and interactive reports to better coordinate care, replacing monthly documents of over 800-pages. Public Health
closely related in the sense that the they too focus on the allocation of a single- dimensional resource. The analyst has available data on how money is divided amongst a fixed number of agents, but has no information on the individuals’ pref- erences and the protocol that leads to the division. They select three theories that could possibly explain the division of money, those of the utilitarian, Nash and egalitarian max-min models (assuming the observed disagreement utility levels are fixed) and show that all three models are observationally equivalent. A main dif- ference is that we characterize rationalizable data as those that satisfy a system of quadratic inequalities and apply the Tarski-Seindenberg algorithm to obtain a test for the NBS (under various hypotheses about the behaviour of the default utility levels), unlike them who use a dual characterization of the problem that satisfies a system of polynomial inequalities. Under the hypothesis that default utility levels vary they apply the Positivstellensatz to construct tests for Nash bargaining and the utilitarian model. Also, Chambers and Echenique only consider a subset of the scenarios that we study here.
In the development phase of CPS, utility potentials are structured on the basis of the target dimensions product quality, manufacturing costs and development time. Field data can be used to achieve a more precise understanding of customer needs and desired product functionalities, particularly in the context of development specific product quality. By analyzing field data, companies can identify and validate delighters. Furthermore performance feature values can be specified more precisely on the basis of field data. When it comes to manufacturing costs, field data can be used to implement a stress-compliant design, which on the one hand increases technical product quality and at the same time reduces manufacturing costs. Another potential by decreasing development time is that CPSs can be employed to validate causes of change as early as possible.
Mining of most useful patterns using interestingness measures play significant role in data mining. Interestingness measures aimed for selecting and ranking patterns according to the users’ interest. Interestingness is a broad concept that highlights conciseness, coverage, reliability, diversity, novelty, surprisingness, utility, and actionability. These criteria further classified into objective, subjective and semantic based. Utility is the one kind of semantic interestingness measure based on utility functions in addition to the raw data. The utility can be measured in terms of cost, profit or other expressions of user preferences. For example, a computer system may be more profitable than a telephone in terms of profit. Business analyst might be interested in extracting all sales with high profit in a transaction database, while another may be interested in finding all transactions with large increase in gross sales.
Take the a text file which contains some data of students pertaining to a regular examination results. Apply the preprocessor and get it ready for the script which is going to be developed. The Pattern matching is done by preparing script using utilities available with unix such as grep and its familly. It is a kind of methods for classification of data, it is used to classify data into predefined groups or classes. With this approach, the grep family utilities are proposed to apply on data warehouse, and to warehouse the result into a temporary file. This intermediate or temporary warehouse can used to mine the knowledge and hence to formulate decisions.
Abstract: With a data revolution underway for some time, there is an increasing demand for formal privacy protection mechanisms that are not so destructive. Hereof microaggregation is a popular high-utility approach designed to satisfy the popular k-anonymity criteria while applying low distortion to data. However, standard performance metrics are commonly based on mean square error, which will hardly capture the utility degradation related to a specific application domain of data. In this work, we evaluate the performance of k-anonymous microaggregation in terms of the loss in classification accuracy of the machine learned models built from perturbed data. Systematic experimentation is carried out on four microaggregation algorithms that are tested over four data sets. The empirical utility of the resulting microaggregated data is assessed using the learning algorithm that obtains the highest accuracy from original data. Validation tests are performed on a test set of non perturbed data. The results confirm k-anonymous microaggregation as a high-utility privacy mechanism in this context and distortion based on mean squared error as a poor predictor of practical utility. Finally, we corroborate the beneficial effects for empirical utility of exploiting the statistical properties of data when constructing privacy preserving algorithms.
In 2005, Ying Liu, W.K. Liao , represented the ARM i.e. Association Rule Mining technique. It discovers the frequent itemsets from the large database and considered individual item to generate association rules. ARM only reflects impact of frequency of the presence and absence of an item. An anti-monotone property is used to discover frequent itemsets. Mining using Expected Utility (MEU) is used to prune the search space by anticipating the high utility k-itemsets. In the section of experimental analysis they analyzed the scalability and accuracy of results. Finally it is seems that in this paper, Two-phase algorithm can efficiently extract HUI.
This research has updated and expanded the scope of previous research conducted by (Lange, 2000). His battery of tests was expanded to incorporate other tests commonly used in assessments by neuropsychologists in their testing of clients with brain dysfunction. The utility of these findings were then assessed, and a new method of classifying cognitive test score profiles was developed. The potential implications for such a classification system are many and include classification of individuals into groups based upon their relative cognitive strengths and weaknesses to better allocate valuable rehabilitation resources to individuals who are most likely to benefit from them, to a new way of considering diagnosis that is better suited to the cognitive and behavioural measures psychologists routinely employ.
III/WMS-III data from a similar sample. The goals of the current dissertation were to: a) replicate Lange’s findings in a larger clinical sample; b) extend the scope of these findings to a wider array of psychological tests; and c) develop a method to classify individual cases in terms of their psychological test profile.
Many applications prefer to divide large jobs into multi- ple small tasks to better utilize the limited resources and provide better service quality. As stated in , most inter- active services such as web search, social networks, online gaming, and financial services now are heavily dependent on computations at data centers because their demands for computing resources are both huge and dynamic. Interactive services are time-sensitive as users expect to receive a complete or possibly partial response within a short period of time. Thus, a job should be preemptive and it can be divided into many small tasks (we call this as multi-task problem) in order to provide interactive ser- vices and improve the utilization ratio of the computing resources.