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We suggest a risk-adjusted valuation approach for workers at retirement risk to make decisions in an overlapping generations economy. The risk-averse workers use greater weights than expected, so-called risk-adjusted probability, on their retirement cash flows to assess the residual lifetime income. This method can be consistently applied to value financial and non-financial assets, and on the risk-adjusted valuations, the workers will make optimal investments. To predict capital returns and economic variables, comparative statics will be numerically implemented via demographic structure, preference, and social security policy by aggregating workers’ decisions.

We suggest an overlapping generations model incorporating the risk-adjusted valuation in a worker’s decision problem. The risk-adjusted probabilities allow risk-averse workers to place more weight on cash flows upon retirement when assessing lifetime income at present value. The risk-adjusted valuation is consistently applied to any non-financial asset, allowing them to be evaluated collectively or separately. We predict capital returns under demographic structure, consumption preference, and social security policy changes.

Recently, large-scale relational data analysis has gained much attention. Several Hadoop-based distributed systems have been proposed for scalable relational data analysis. Because the column-store approach is very suitable for analytic queries, many studies on column-oriented storage and query processing for Hadoop-based distributed systems have been conducted. However, two problems have arisen in existing studies, the first of which is that only a small amount of data is processed per task during distributed processing. Each task reads only the necessary data using the columnar structure. Because the task initialization in Hadoop requires a large overhead, it is inefficient that each task processes a small amount of data. The second problem is the lack of support for techniques that optimize columnar execution. Although many such techniques have been proposed for efficient columnar query execution, existing column-store methods for Hadoop-based distributed systems cannot support them efficiently. In this paper, we propose a novel column-store method called SSFile for Hadoop-based distributed systems. SSFile increases the actual amount of data processed per task and supports representative columnar execution techniques for efficient query processing. Through extensive experiments, we show that SSFile significantly improves the performance of distributed processing.

In this paper, we introduce a novel spatial query called the view field nearest neighbor query. Given the view field and location of a user, the view field nearest neighbor query retrieves a data object that is nearest to the user’s location and falls within the user’s view field. This query can be employed for applications such as augmented reality systems, tour guide systems, and CCTV-based surveillance systems. We propose a view field nearest neighbor query processing method that considers moving data objects (i.e., continuous view field nearest neighbor query processing), where we utilize the grid index. Continuous view field nearest neighbor query processing consists of two phases: (1) initial phase and (2) update phase. The first phase computes the initial result of a view field nearest neighbor query (i.e., snapshot query result) and the second phase continuously updates the result according to the movement of data objects. For the initial phase, we propose two algorithms: Naive Exploration Algorithm and Fan-shaped Exploration Algorithm. For the update phase, we propose the Fan-shaped Monitoring Algorithm to process the moving data objects efficiently. Through extensive experiments, we investigate the performance of our proposed algorithms on synthetic and real data sets.