Incremental elasticity for array databases

Relational databases benefit significantly from elasticity, whereby they execute on a set of changing hardware resources provisioned to match their storage and processing requirements. Such flexibility is especially attractive for scientific databases because their users often have a no-overwrite storage model, in which they delete data only when their available space is exhausted. This results in a database that is regularly growing and expanding its hardware proportionally. Also, scientific databases frequently store their data as multidimensional arrays optimized for spatial querying. This brings about several novel challenges in clustered, skew-aware data placement on an elastic shared-nothing database. In this work, we design and implement elasticity for an array database. We address this challenge on two fronts: determining when to expand a database cluster and how to partition the data within it. In both steps we propose incremental approaches, affecting a minimum set of data and nodes, while maintaining high performance. We introduce an algorithm for gradually augmenting an array database's hardware using a closed-loop control system. After the cluster adds nodes, we optimize data placement for n-dimensional arrays. Many of our elastic partitioners incrementally reorganize an array, redistributing data only to new nodes. By combining these two tools, the scientific database efficiently and seamlessly manages its monotonically increasing hardware resources.Intel Corporation (Science and Technology Center for Big Data

Do Financial Incentives Affect Fertility?

This paper investigates how fertility responds to changes in the price of a marginal child and in household income. We construct a large, individual-level panel data set of married Israeli women during the period 1999–2005 that contains fertility histories and detailed controls. We exploit variation in Israel’s child subsidy program to identify changes in the price of a marginal child (using changes in the subsidy for a marginal child) and to instrument for household income (using changes in the subsidy for infra-marginal children). We find a significant and positive price effect on fertility: the mean level of marginal child subsidy produces a 7.8 percent increase in fertility. There is a positive effect within all religious and ethnic subgroups, including the ultra-Orthodox Jewish population, whose social and religious norms discourage family planning. There is also a significant price effect on fertility among women who are close to the end of their lifetime fertility, suggesting that at least part of the price effect is due to a reduction in total fertility. As expected, the child subsidy has no effect in the upper range of the income distribution. Finally, consistent with the predictions of Becker (1960) and Becker and Tomes (1976), we find that the income effect is small in magnitude and is negative at low income levels and positive at high levels.

Taxation and Market Work: Is Scandinavia an Outlier?

This paper argues that it is essential to explicitly consider how the government spends tax revenues when assessing the effects of tax rates on aggregate hours of market work. Different forms of government spending imply different elasticities of hours of work with regard to tax rates. I illustrate the empirical importance of this point by addressing the issue of hours worked and tax rates in three sets of economies: the US, Continental Europe and Scandinavia. While tax rates are highest in Scandinavia, hours worked in Scandinavia are significantly higher than they are in Continental Europe. I argue that differences in the form of government spending can potentially account for this pattern.

A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database

Physics-constrained data-driven computing is an emerging hybrid approach that integrates universal physical laws with data-driven models of experimental data for scientific computing. A new data-driven simulation approach coupled with a locally convex reconstruction, termed the local convexity data-driven (LCDD) computing, is proposed to enhance accuracy and robustness against noise and outliers in data sets in the data-driven computing. In this approach, for a given state obtained by the physical simulation, the corresponding optimum experimental solution is sought by projecting the state onto the associated local convex manifold reconstructed based on the nearest experimental data. This learning process of local data structure is less sensitive to noisy data and consequently yields better accuracy. A penalty relaxation is also introduced to recast the local learning solver in the context of non-negative least squares that can be solved effectively. The reproducing kernel approximation with stabilized nodal integration is employed for the solution of the physical manifold to allow reduced stress-strain data at the discrete points for enhanced effectiveness in the LCDD learning solver. Due to the inherent manifold learning properties, LCDD performs well for high-dimensional data sets that are relatively sparse in real-world engineering applications. Numerical tests demonstrated that LCDD enhances nearly one order of accuracy compared to the standard distance-minimization data-driven scheme when dealing with noisy database, and a linear exactness is achieved when local stress-strain relation is linear

Development and use of an integrated systems model to design technology strategies for energy services in rural developing communities

For the 40% of the world\u27s families living in energy poverty today, energy services are provided almost exclusively by the same three-stone fires that have been used for millennia. The pollution from the pervasive use of these fires represents the second leading cause of death for women worldwide and contributes significantly to local and global climate change. Improving access to clean energy services can facilitate improved health and livelihoods and serve as a precursor to other economic and social development. Yet within these diverse, complex, and highly-localized communities, the most effective strategies to provide clean energy are not clear; and success of programs to provide technologies such as biomass cookstoves or subsidize fuels such as LPG or electricity has often been limited. This is because an energy carrier or conversion technology is only a small component of a much larger energy system that includes a complex set of needs, constraints, and other variables at the household, community, and global scales. Within this system exists a range of technical, economic, social, and environmental objectives that often conflict between these scales to create an imbalance between stakeholders; and outcomes vary widely based on technology design choices and local conditions. As a result, development of effective solutions requires a clear understanding of the direct and indirect impacts of design decisions that are rooted in the fundamental interactions between energy, the environment, and people. In order to assist in understanding these interactions in a systematic fashion, this dissertation develops a probabilistic unified modeling approach that seeks to facilitate energy system design by predicting outcomes in terms of a set of multi-disciplinary considerations and objectives. This approach incorporates a large parameter space including local energy needs, demographics, fuels, and devices to create a comprehensive analysis of potential strategies in terms of a range of technical, environmental, economic, and social outcomes. While recognizing that there is no single \u27best\u27 solution, this methodology allows the designer to investigate and understand trade-offs between conflicting and competing objectives, the effects of usability and multi-functionality, sensitivities of input parameters for identification of prominent and critical factors, the impacts of uncertainty in decision-making, and the potential for compromise and integrated strategies that provide sustainable and effective energy services. The model is used to explore a number of scenarios to provide energy services in a remote off-grid village in Mali for which detailed measures of disaggregated energy use are available. In addition to detailed analysis of the baseline situation, strategies investigated include the introduction of (1) general improved biomass cookstoves, (2) advanced biomass cookstoves, (3) communal biomass cookstoves, (4) LPG cookstoves, (5) solar water heaters, and (6) community-charged solar household lighting. Following this and other analyses, an integrated strategy for energy services is developed. The results show that the factors with the largest impact on the outcome of a technology strategy include the rate of user adoption, value of time, and biomass harvest renewability; in contrast, parameters such as cookstove emission factors may have less impact on the outcome. This suggests that the focus of village energy research and development should shift to the design of technologies that have high expected user adoption rates. That is, the results of this study support the hypothesis that the most effective village energy strategy is one that reinforces the natural user-driven process to stack technologies while moving toward efficient and convenient energy services. A comprehensive strategy that provides the current state-of-the art technologies to optimally meet each specific energy need in the Malian village with a population of 770—including advanced cookstoves, LPG cookstoves, solar water heaters, and solar battery lighting systems—is expected to annually create 2.5 TJ of energy savings, 500 metric tons of CO2e savings, a 40% reduction in health risk, and offer substantial improvement of quality of life. Moreover, this strategy will reduce operating costs to the users including time by an estimated $1,000 (US) each year. Such a strategy is expected to cost$12-$13 per person per year to purchase and maintain the necessary technologies if supplied by outside financing, a figure which might double or triple when implementation costs are included. This is a relatively small expense in comparison to the projected cost of$110 per person per year to provide the necessary agricultural, health, and educational inputs needed for the Millennium Villages, a figure reported to be well within the range committed by international aid organizations

Agricultural Research and Poverty Alleviation: Some International Perspectives

Invited paper for the John L. Dillon AO Commemorative Day on ‘Agricultural Research: Challenges and Economics in the New Millenium’ The University of New England, Armidale NSW Australia, September 20, 2002Food Security and Poverty, Research and Development/Tech Change/Emerging Technologies,

BALANCING PRIVACY, PRECISION AND PERFORMANCE IN DISTRIBUTED SYSTEMS

Privacy, Precision, and Performance (3Ps) are three fundamental design objectives in distributed systems. However, these properties tend to compete with one another and are not considered absolute properties or functions. They must be defined and justified in terms of a system, its resources, stakeholder concerns, and the security threat model. To date, distributed systems research has only considered the trade-offs of balancing privacy, precision, and performance in a pairwise fashion. However, this dissertation formally explores the space of trade-offs among all 3Ps by examining three representative classes of distributed systems, namely Wireless Sensor Networks (WSNs), cloud systems, and Data Stream Management Systems (DSMSs). These representative systems support large part of the modern and mission-critical distributed systems. WSNs are real-time systems characterized by unreliable network interconnections and highly constrained computational and power resources. The dissertation proposes a privacy-preserving in-network aggregation protocol for WSNs demonstrating that the 3Ps could be navigated by adopting the appropriate algorithms and cryptographic techniques that are not prohibitively expensive. Next, the dissertation highlights the privacy and precision issues that arise in cloud databases due to the eventual consistency models of the cloud. To address these issues, consistency enforcement techniques across cloud servers are proposed and the trade-offs between 3Ps are discussed to help guide cloud database users on how to balance these properties. Lastly, the 3Ps properties are examined in DSMSs which are characterized by high volumes of unbounded input data streams and strict real-time processing constraints. Within this system, the 3Ps are balanced through a proposed simple and efficient technique that applies access control policies over shared operator networks to achieve privacy and precision without sacrificing the systems performance. Despite that in this dissertation, it was shown that, with the right set of protocols and algorithms, the desirable 3P properties can co-exist in a balanced way in well-established distributed systems, this dissertation is promoting the use of the new 3Ps-by-design concept. This concept is meant to encourage distributed systems designers to proactively consider the interplay among the 3Ps from the initial stages of the systems design lifecycle rather than identifying them as add-on properties to systems