Approximate Answers Research Articles

An iterative sampling method for online aggregation

Online aggregation (OLA) makes it possible to save cost by taking acceptable approximate early answers. Compared to the precise results, computing the approximate ones are more cost effective, especially for large-scale datasets. The user can terminate the processing at any time, when he/she is satisfied with the quality of the result. And the performance of OLA relies on the sampling approach and estimation model. But in large scale distributed computing environment, how to realize OLA more efficiently is a challenging problem. In this paper, we consider the problem of providing OLA in the distributed computing environment and propose a Hadoop-based iterative sampling method for online aggregation. The desired precision of the user can be met by two iteration samplings. To avoid the effects of data bias, we propose a “layered sampling” method to ensure that the approximate aggregation result is statistically meaningful. The experimental results showed the “layered sampling” method considers not only the time efficiency, but also the usage of computing and storage resources of Hadoop.

Uncertainty estimation with a small number of measurements, part II: a redefinition of uncertainty and an estimator method

This paper is the second (Part II) in a series of two papers (Part I and Part II). Part I has quantitatively discussed the fundamental limitations of the t-interval method for uncertainty estimation with a small number of measurements. This paper (Part II) reveals that the t-interval is an ‘exact’ answer to a wrong question; it is actually misused in uncertainty estimation. This paper proposes a redefinition of uncertainty, based on the classical theory of errors and the theory of point estimation, and a modification of the conventional approach to estimating measurement uncertainty. It also presents an asymptotic procedure for estimating the z-interval. The proposed modification is to replace the t-based uncertainty with an uncertainty estimator (mean- or median-unbiased). The uncertainty estimator method is an approximate answer to the right question to uncertainty estimation. The modified approach provides realistic estimates of uncertainty, regardless of whether the population standard deviation is known or unknown, or if the sample size is small or large. As an application example of the modified approach, this paper presents a resolution to the Du–Yang paradox (i.e. Paradox 2), one of the three paradoxes caused by the misuse of the t-interval in uncertainty estimation.

Ganser Syndrome.

Ganser's syndrome is a rare and controversial condition, whose main and most striking feature is the production of approximate answers (or near misses) to very simple questions. For instance, asked how many legs a horse has, Ganser patients will reply "5", and answers to plain arithmetic questions will likewise be wrong, but only slightly off the mark (e.g., 2 + 2 = 3). This symptom was originally described by Sigbert Ganser in 1897 in prisoners on remand and labeled Vorbeigehen ("to pass by"), although the term Vorbeireden ("to talk beside the point") is also frequently used. A number of associated symptoms were also reported: "clouding of consciousness," somatoform conversion disorder, hallucinations, sudden and spontaneous recovery, subsequent amnesia for the episode, premorbid traumatic psychosocial experience and/or (usually mild) head trauma. Etiological, epidemiological and diagnostic issues have never been resolved for Ganser's syndrome. Ganser saw it as a form of "twilight hysteria," whereas others suggested that malingering, psychosis or dissociation were more appropriate labels, oftentimes combined with organic impairment and a subjectively intolerable psychosocial context. A central conundrum of Ganser's syndrome is whether it could simultaneously be a cultural and pathological representation of insanity, whereas cognitive, organic, affective, motivational and social factors would converge towards a naïve idea of what mental illness should look like, especially through the provision of approximate answers.

Privacy-Preserving Location Sharing Services for Social Networks

A common functionality of many location-based social networking applications is a location sharing service that allows a group of friends to share their locations. With a potentially untrusted server, such a location sharing service may threaten the privacy of users. Existing solutions for Privacy-Preserving Location Sharing Services (PPLSS) require a trusted third party that has access to the exact location of all users in the system or rely on expensive algorithms or protocols in terms of computational or communication overhead. Other solutions can only provide approximate query answers. To overcome these limitations, we propose a new encryption notion, called Order-Retrievable Encryption (ORE), for PPLSS for social networking applications. The distinguishing characteristics of our PPLSS are that it (1) allows a group of friends to share their exact locations without the need of any third party or leaking any location information to any server or users outside the group, (2) achieves low computational and communication cost by allowing users to receive the exact location of their friends without requiring any direct communication between users or multiple rounds of communication between a user and a server, (3) provides efficient query processing by designing an index structure for our ORE scheme, (4) supports dynamic location updates, and (5) provides personalized privacy protection within a group of friends by specifying a maximum distance where a user is willing to be located by his/her friends. Experimental results show that the computational and communication cost of our PPLSS is much better than the state-of-the-art solution.

Probabilistic database summarization for interactive data exploration

We present a probabilistic approach to generate a small, query-able summary of a dataset for interactive data exploration. Departing from traditional summarization techniques, we use the Principle of Maximum Entropy to generate a probabilistic representation of the data that can be used to give approximate query answers. We develop the theoretical framework and formulation of our probabilistic representation and show how to use it to answer queries. We then present solving techniques and give three critical optimizations to improve preprocessing time and query accuracy. Lastly, we experimentally evaluate our work using a 5 GB dataset of flights within the United States and a 210 GB dataset from an astronomy particle simulation. While our current work only supports linear queries, we show that our technique can successfully answer queries faster than sampling while introducing, on average, no more error than sampling and can better distinguish between rare and nonexistent values.

Data driven approximation with bounded resources

This paper proposes BEAS, a resource-bounded scheme for querying relations. It is parameterized with a resource ratio α ∈ (0,1], indicating that given a big dataset D , we can only afford to access an α -fraction of D with limited resources. For a query Q posed on D , BEAS computes exact answers Q(D) if doable and otherwise approximate answers, by accessing at most α | D | amount of data in the entire process. Underlying BEAS are (1) an access schema, which helps us identify and fetch the part of data needed to answer Q , (2) an accuracy measure to assess approximate answers in terms of their relevance and coverage w.r.t . exact answers, (3) an Approximability Theorem for the feasibility of resource-bounded approximation, and (4) algorithms for query evaluation with bounded resources. A unique feature of BEAS is its ability to answer unpredictable queries, aggregate or not, using bounded resources and assuring a deterministic accuracy lower bound. Using real-life and synthetic data, we empirically verify the effectiveness and efficiency of BEAS.

Ganser's syndrome: A nosographic approach

IntroductionGanser described a peculiar hysterical state, called Ganser's syndrome. This syndrome raises many etiological and psychopathological unresolved issues.ObjectivesThis article proposes to present the place of the Ganser's syndrome in the current nosographic framework throw the analyse of a clinical case and a literature review.ObservationA 28-year-old man was admitted for fugue and memory loss. This symptomatology evolves since three days after an emotional conflict.He complained from headache. He showed incoherent speech with approximate responses, lability, anxiety, auditory hallucinations, unstructured mild delusional ideation, cognitive difficulties, altered sleep-wake rhythm and anorexia.Memory gaps were observed with difficulties in abstract thinking.Symptoms totally regressed after one week under anxiolytic treatment.CommentsGanser's syndrome was evoked in the presence of suggestive symptoms: presence of a stressor factor, cardinal symptoms (approximate answers), associated symptoms (hallucinations + confusion + somatoform symptoms) and rapid restitution. Ganser considered this syndrome as a special case of crepuscular state, belonging to hysteria. Ganser's syndrome was included in DSM-III but located in factitious disorders against Ganser's position. In DSM-IV, it was positioned in unspecified dissociative disorders. In DSM-5, its place was reduced to a few words in the end of the introduction of dissociative disorders, and was no longer used as dissociative disorder.ConclusionAlthough Ganser's syndrome is not part of current diagnostic criteria for dissociative disorders, clinical descriptions of Ganser remain of clinical of interest by nosographic questions they have raised, in particular the link between simulation, psychiatric disorder and non psychiatric disorder.Disclosure of interestThe authors have not supplied their declaration of competing interest.

A systematic report review of Ganser syndrome: 118 years of case studies

IntroductionGanser syndrome was first described by a German psychiatrist Ganser in 1898, in a patient who showed a peculiar twilight state. Ganser syndrome is defined as the presence of approximate answers, somatic conversion symptoms, clouding of consciousness, and pseudo-hallucinations. The etiology of this disease remains a subject of debate. While the DSM-IV-TR classifies Ganser syndrome under the heading of dissociative disorder, it is not listed as a diagnosis in the DSM-V.Objectives and aimsThe purpose of this paper is to review available literature on Ganser syndrome, published in Dutch, English, German, and French for examining the etiological debate, in order to gain insight into the etiology of this disorder.MethodsThe study design was a retrospective case series of all published cases since 1898. For this purpose we used the electronic databases PubMed and Embase.ResultsOver a period of 118 years, we found 79 papers, describing 117 case reports on Ganser syndrome. It generally occurs in patients who are exposed to somatic disorders or to psychological stress, however, often in absence of a psychiatric disorder.ConclusionsGanser syndrome remains a controversial disorder in terms of its etiology. Ganser syndrome has been associated with organic disorders, as well as with stressful and intolerable life events. Based on this report, it is noteworthy that this syndrome predominantly occurs in the absence of co-morbid psychiatric disorders and is often associated with stress factors and underlying somatic diseases.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Approximate answering of queries involving polyline–polyline topological relationships

In geographic information systems, pictorial query languages are visual languages which make easier the user to express queries by free-hand drawing. In this perspective, this article proposes an approach to provide approximate answers to pictorial queries that do not match with the content of the database, that is, the results are null. It addresses the polyline–polyline topological relationships and is based on an algorithm, called Approximate Answer Computation algorithm, which exploits the notions of Operator Conceptual Neighborhood graph and 16-intersection matrix. The operator conceptual neighborhood graph represents the conceptual topological neighborhood between Symbolic Graphical Objects and is used for relaxing constraints of queries. The nodes of the operator conceptual neighborhood graph are labeled with geo-operators whose semantics has been formalized. The 16-intersection matrix provides enriched query details with respect to the well-known Dimensionally Extended 9-Intersection Model proposed in the literature. A set of minimal 16-intersection matrices associated with each node of the operator conceptual neighborhood graph, upon the external space connectivity condition, is defined and the proof of its minimality is provided. The main idea behind each introduced notion is illustrated using a running example throughout this article.

Option Pricing via QUAD: From Black–Scholes–Merton to Heston with Jumps

The Black–Scholes model is the rare closed-form formula for pricing options, but its shortcomings are well known. Adding stochastic volatility, American or Bermudan early exercise, non-diffusive jumps in the returns process, and/or other “exotic” payoff features quickly takes one into the realm where approximate answers must by computed by numerical methods. Binomial and other lattice models, Monte Carlo simulation, and numerical solution of the valuation partial differential equation are the standard techniques. They are well known to eventually arrive at as close an approximation to the exact model value as one wants but can require a large amount of calculation and execution time to get there. In this article, the authors present what amounts to the culmination of a series of papers on using quadrature methods to solve option problems of increasing complexity. Quadrature entails modeling and approximating the transition densities between critical points in the option’s lifetime. Depending on the specifics of the option’s payoff, looking at only a few points in time can greatly reduce the amount of calculation needed to price it. For example, a Bermudan option that can be exercised at three possible early dates needs quadrature calculations for only those three dates plus expiration, while other numerical methods require calculations for every date and possible asset price. This article summarizes how to apply quadrature to standard option problems, from plain-vanilla Black–Scholes up to Bermudan and American options under a stochastic volatility jump-diffusion returns process. The procedure can be speeded up even more by use of Richardson extrapolation and caching techniques that allow reuse of results calculated in intermediate steps. The range of derivatives models that can be valued extremely efficiently using quadrature is very broad.

Vaccination allocation in large dynamic networks

Network infections that are already in progress cause challenges to those officers trying to preserve those nodes not yet infected. Static solutions can take advantage of global knowledge of the network to produce quick and approximate answers for those members who should be vaccinated. In dynamic situations however, small changes can severely alter those static solutions making them irrelevant. Yet in dynamic situations it can not be known with certainty which small changes will affect the solution and those that will not. Computational resources are wasted recalculating a global solution for the entire network, when a local recalculation may be enough. This paper presents a dynamic node vaccination solution that seeks to take advantage of these local recalculations.

Analytical Solutions of Heat Problems for Efficient Heat Transfer in a Nanofluid

To find the approximate solutions of heat equations in a boundary layer flow, beneath a uniform free stream permeable continuous moving surface in a nanofluid is the main purpose of this paper. First, we will propose a neural network coupled with the Chebyshev polynomials. We will then study the heat transfer and heat flow equations by using the presented Chebyshev neural network. As it turns out, this method can obtain the approximate solutions for any kind heat transfer and heat flow equations. Approximate answers can be more helpful to study the behavior of heat transfer heat flow, and it can ensure a more efficient heat transfer with a lower operational cost. The missing slopes f rr(0) and gr(0), for some values of the governing parameters, namely the nano-particle volume fraction φ, the mov- ing parameter λ and the suction/injection parameter f0 are determined using the proposed method. The obtained results of this method have been compared with other papers results of different methods.

Fast, Ad Hoc Query Evaluations over Multidimensional Geospatial Datasets

Networked observational devices and remote sensing equipment continue to proliferate and contribute to the accumulation of extreme-scale datasets. Both the rate and resolution of the readings produced by these devices have grown over time, exacerbating the issues surrounding their storage and management. In many cases, the sheer scale of the information being maintained makes timely analysis infeasible due to the computational workloads required to process the data. While distributed solutions provide a scalable way to cope with data volumes, the communication and latency involved when inspecting large portions of an overall dataset limit applications that require frequent or rapid responses to incoming queries. This study investigates the challenges associated with providing approximate or exploratory answers to distributed queries. In many situations, this requires striking a balance between response times and error rates to produce meaningful results. To enable these use cases, we outline several expressive query constructs and describe their implementation; rather than relying on summary tables or pre-computed samples, our solution involves a coarse-grained global index that maintains statistics and models the relationships across dimensions in the dataset. To illustrate the benefits of these techniques, we include performance benchmarks on a real-world dataset in a production environment.

Indian Interest in the Arctic in the Context of China’s Arctic Policy

The study examines the general and excellent in the Arctic policy of India and China and the likelihood of rivalry between the two Asian powers in the allocation of resources to the Arctic. Also, Indian and Chinese research trends on Arctic topics were considered. Since the middle of XX century. India and China act as long-standing rivals. Periodically, military clashes broke out between the two sides on a common border. Despite the fact that the key territorial issues on the common border between India and China are resolved military provocations from both sides do not stop. The latest incident was the transfer of Indian troops to the Chinese border zone in the province of Sikkim. Both powers of Asia are major consumers of energy resources, they are more or less interested in the situation on the global energy market. In the Arctic there are colossal reserves of various resources. Certain difficulties and limitations with access to resources and their equitable distribution can force these countries to compete with each other. However, the lack of a specific position on Arctic issues or the formulated regional strategy for India and China creates a lot of doubt about their true intentions. The study of research trends, the activity of Indian and Chinese business structures, as well as the arctic activities of India and China, gives approximate answers to this question. The potentials of India and China are very different in the Arctic. China’s strengths are active participation in international scientific research, the availability of a qualified ice-class crew, active investment in energy and infrastructure projects in the Arctic countries, and fairly stable trade relations with most Arctic countries. The weak side of China is its negative image. Residents of many Arctic countries are very wary of the «rise of China» and its growing interest in the Arctic. Strengths of India are a positive image and a representative diaspora in the Arctic countries, especially in the US and Canada. In the future this will allow Delhi to successfully promote initiatives in the Arctic. The weak side of India can be considered the absence of a ship «icebreaker class», the weak investment activity of Indian business structures and poor knowledge of Arctic problems from the point of view of Indian issues.

Approximate Continuous Top-k Query over Sliding Window

Continuous top-k query over sliding window is a fundamental problem in database, which retrieves k objects with the highest scores when the window slides. Existing studies mainly adopt exact algorithms to tackle this type of queries, whose key idea is to maintain a subset of objects in the window, and try to retrieve answers from it. However, all the existing algorithms are sensitive to query parameters and data distribution. In addition, they suffer from expensive overhead for incremental maintenance, and thus cannot satisfy real-time requirement. In this paper, we define a novel query named (e, δ)-approximate continuous top-k query, which returns approximate answers for top-k query. In order to efficiently support this query, we propose an efficient framework, named PABF (Probabilistic Approximate Based Framework), to support approximate top-k query over sliding window. We firstly maintain a self-adaptive pruning value, which could filter out newly arrived objects who have a probability less than 1 − δ of being a query result. For those objects that are not filtered, we combine them together, if the score difference among them is less than a threshold. To efficiently maintain these combined results, the framework PABF also proposes a multi-phase merging algorithm. Theoretical analysis indicates that even in the worst case, we require only logarithmic complexity for maintaining each candidate.

Incremental Query Processing on Big Data Streams

This paper addresses online query processing for large-scale, incremental data analysis on a distributed stream processing engine (DSPE). Our goal is to convert any SQL-like query to an incremental DSPE program automatically. In contrast to other approaches, we derive incremental programs that return accurate results, not approximate answers. This is accomplished by retaining a minimal state during the query evaluation lifetime and by using incremental evaluation techniques to return an accurate snapshot answer at each time interval that depends on the current state and the latest batches of data. Our methods can handle many forms of queries on nested data collections, including iterative and nested queries, group-by with aggregation, and equi-joins. Finally, we report on a prototype implementation of our framework, called MRQL Streaming, running on top of Spark and we experimentally validate the effectiveness of our methods.

DistR: A Distributed Method for the Reachability Query over Large Uncertain Graphs

Among uncertain graph queries, reachability, i.e., the probability that one vertex is reachable from another, is likely the most fundamental one. Although this problem has been studied within the field of network reliability, solutions are implemented on a single computer and can only handle small graphs. However, as the size of graph applications continually increases, the corresponding graph data can no longer fit within a single computer's memory and must therefore be distributed across several machines. Furthermore, the computation of probabilistic reachability queries is #P-complete making it very expensive even on small graphs. In this paper, we develop an efficient distributed strategy, called DistR , to solve the problem of reachability query over large uncertain graphs. Specifically, we perform the task in two steps: distributed graph reduction and distributed consolidation . In the distributed graph reduction step, we find all of the maximal subgraphs of the original graph, whose reachability probabilities can be calculated in polynomial time, compute them and reduce the graph accordingly. After this step, only a small graph remains. In the distributed consolidation step, we transform the problem into a relational join process and provide an approximate answer to the #P-complete reachability query. Extensive experimental studies show that our distributed approach is efficient in terms of both computational and communication costs, and has high accuracy.

Fundamentals and Recent Developments in Approximate Bayesian Computation.

Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible. We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments. [ABC; approximate Bayesian computation; Bayesian inference; likelihood-free inference; phylogenetics; simulator-based models; stochastic simulation models; tree-based models.]

Actuarial Implications from Pre-kindergarten Education

Great progress has been made in providing pre-kindergarten (pre-K) public education throughout the United States. The percentages of 3- and 4-year-olds enrolled nationally have grown from 3% to 5% and 14% to 29%, respectively, between 2002 and 2015. By 2015, 42 states and the District of Columbia were in varying stages of offering pre-K programs (0.9% to 74.2% for totals of 3- and 4-year-olds); eight states were in stages of implementation. We will provide approximate answers to four questions. The first two are how does pre-K education affect female and male life expectancies? The other two are how does pre-K education affect expected years of life dependency in health and in lifetime earnings? The methodology used to help answer these questions consisted of using actuarial/demographic tables over the years 1990 to 2040. It will be shown that upper limits to estimated increases in male and female life expectancy that can be attributed to pre-K education are 2.47 and 1.67 years, respectively. Moderate estimates to the decreases in expected years of health dependency for 65-year old males and females that benefit from pre-K education are 1.47 and 4.71 years, respectively. We will document that people with pre-K education will have higher high school graduation rates, lower crime rates, higher employment rates, and higher wages than those without pre-K education; these four improved rates will lead to improved life expectancies and diminished years of health dependency. These results have actuarial implications for life insurance, long-term health insurance, and pension premium calculations.

How Progressive Visualizations Affect Exploratory Analysis.

The stated goal for visual data exploration is to operate at a rate that matches the pace of human data analysts, but the ever increasing amount of data has led to a fundamental problem: datasets are often too large to process within interactive time frames. Progressive analytics and visualizations have been proposed as potential solutions to this issue. By processing data incrementally in small chunks, progressive systems provide approximate query answers at interactive speeds that are then refined over time with increasing precision. We study how progressive visualizations affect users in exploratory settings in an experiment where we capture user behavior and knowledge discovery through interaction logs and think-aloud protocols. Our experiment includes three visualization conditions and different simulated dataset sizes. The visualization conditions are: (1) blocking, where results are displayed only after the entire dataset has been processed; (2) instantaneous, a hypothetical condition where results are shown almost immediately; and (3) progressive, where approximate results are displayed quickly and then refined over time. We analyze the data collected in our experiment and observe that users perform equally well with either instantaneous or progressive visualizations in key metrics, such as insight discovery rates and dataset coverage, while blocking visualizations have detrimental effects.