Inverted index maintenance strategy for flashSSDs: Revitalization of in-place index update strategy

An inverted index is a core data structure of Information Retrieval systems, especially in search engines. Since the search environments have become more dynamic, many on-line index maintenance strategies have been proposed. Previous strategies were designed for HDDs. Consequently, in order to avoid expensive random access cost, Merge-based strategies have been preferred to In-place index update strategies on HDDs. However, flashSSDs have become solid alternatives to HDDs. FlashSSDs currently are adopted in a wide range of areas due to their superior features such as the short access latency, energy efficiency, and high bandwidth. In this article, we first reexamined potentials of In-place index update strategies on flashSSDs. Thanks to the insignificant access latency of flashSSDs, we discovered that In-place index update strategies outperform Merge-based strategies, since In-place index update strategies generate much less amount of I/O than Merge-based strategies despite inducing frequent random accesses. Based on this discovery, we suggest a new inverted index maintenance strategy based on an In-place index update strategy for flashSSDs, called Multipath Flash In-place Strategy (MFIS). To enhance the index maintenance performance, MFIS stores the posting list of each term non-contiguously and exploits the internal parallelism of flashSSDs. Thus, MFIS not only induces the minimum amount of I/O but also utilizes the maximum bandwidth of flashSSDs. Furthermore, MFIS is designed to show high query processing performance by utilizing the internal parallelism of flashSSDs even though the posting list of each term is stored non-contiguously. In our experiments, the index maintenance performance of MFIS was considerably better than other previous maintenance strategies. The index maintenance performance was up to 14.93, 4.04, 5.12, and 2.33 times higher than Merge-based strategies such as Immediate Merge, Geometric Partitioning, Hybrid, and SSD-aware Hybrid, respectively. The query processing performance of MFIS was up to 1.62 times higher than non-contiguous In-place. In addition, MFIS showed almost the best query processing performance as Merge-based strategies did. In conclusion, MFIS is the best on-line inverted index maintenance strategy on flashSSDs in terms of both index maintenance and query processing performance.     

KMoS-RE: knowledge management on a strategy to requirements engineering

This paper introduces the KMoS-RE Strategy, a novel requirements engineering strategy to Informal Structured Domains. Requirements engineering aims to elicit, analyze, evaluate, consolidate, and manage the requirements of a software system. The complexity of this process depends on the application domain. In Informal Structured Domains, not all concepts and their relations are formally defined, most of the problems do not have algorithms to obtain solutions, and the domain specialists use large amounts of tacit knowledge to solve problems. These characteristics generate ambiguous, inappropriate, and incomplete requirements. It could generate an inadequate software solution, or it could be the cause of increasing the project development time. Therefore, it is important to use an appropriate requirements engineering strategy to minimize these problems. The objective of this study is to present the Knowledge Management on a Strategy to Requirements Engineering (KMoS-RE Strategy): a novel requirements engineering strategy oriented to the transformation and transference of knowledge, and with the aim to minimize the percentage of ambiguous, incomplete, and inappropriate requirements. The functionality and utility of the strategy is explained through its application to a real case study. The case study shows that using the KMoS-RE Strategy helps to internalize the domain knowledge, to clarify the solution idea, to reduce the ignorance of symmetry, to structure the domain knowledge, and to detect and correct wrong beliefs about the domain. All of these are performed in the early stage of the project development.     

Knowledge discovery in molecular biology: Identifying structural regularities in proteins

In recent years, there has been an explosive amount of molecular biology information obtained and deposited in various databases. Identifying and interpreting interesting patterns from this massive amount of information has become an essential component in directing further molecular biology research. The goal of this research is to discover structural regularities in protein sequences by applying the SUBDUE discovery system to databases found in the Brookhaven Protein Data Bank. In this paper we discuss issues relevant to this application including data preparation and representation. We report on the results of applying SUBDUE to several classes of protein structures and discuss the potential significance of these results in the study of proteins.     

On the use of shared task models in knowledge acquistion,strategic user interaction and clarification agents

In this paper, three different roles of a shared task model as an intermediate representation of a task are presented and illustrated by applications developed in cooperation with industry. First the role of a shared task model in knowledge acquisition is discussed. In one of the two applications, decision support in the domain of soil sanitation, one of the existing generic task models for diagnostic reasoning provided a means to structure knowledge acquisition. In the second application, diagnosis of chemical processes, the acquisition process resulted in a shared task model for diagnostic reasoning on Nylon-6 production. Secondly, the role of a shared task model in designing user interaction is addressed. Three levels of interaction are considered of importance: interaction at the object level, at the level of strategic preferences, and at the level of task modification. In an application in the domain of environmental decision making, this led to the design of a user interface based on the acquired shared task model, within which all three levels of interaction were available to users. Finally, the role of shared task models within a multi-agent system including a clarification agent is addressed. Two software agents were designed that each share a task model with the user: one for a diagnosis task, and one for a clarification task. The shared model of the clarification task reflects the shared task model of diagnosis; clarification includes clarification of the overall diagnostic reasoning process. The multi-agent architecture presented has been developed to support a user both at the level of the diagnostic task he or she is performing and at the level of clarification. The architecture has been applied to the diagnosis of chemical processes.     

Verifiable strategy synthesis for multiple autonomous agents: a scalable approach

International Journal on Software Tools for Technology Transfer - Path planning and task scheduling are two challenging problems in the design of multiple autonomous agents. Both problems can be...     

Shape equilibrium constraint: a strategy for stress-constrained structural topology optimization

In topology optimization of a continuum, it is important to consider stress-related objective or constraints, from both theoretical and application perspectives. It is known that the problem is challenging. Although remarkable achievements have been made with the SIMP (Solid Isotropic Material with Penalization) framework, a number of critical issues are yet to be fully resolved. In the paper, we present an approach of a shape equilibrium constraint strategy with the level-set/X-FEM framework. We formulate the topology optimization problem under (spatially-distributed) stress constraints into a shape equilibrium problem of active stress constraint. This formulation allows us to effectively handle the stress constraint, and the intrinsic non-differentiability introduced by local stress constraints is removed. The optimization problem is made into one of continuous shape-sensitivity and it is solved by evolving a coherent interface of the shape equilibrium concurrently with shape variation in the structural boundary during a level-set evolution process. Several numerical examples in two dimensions are provided as a benchmark test of the proposed shape equilibrium constraint strategy for minimum-weight and fully-stressed designs and for designs with stress constraint satisfaction.     

Determinants of academic use of the Internet: a structural equation model

The last decade of the 20th century brought radical changes in information and communication technology. Internet usage is being widely researched in the business world. However, the use of the Internet in academic settings in general and in vocational and technical establishment in particular is a neglected area. Successful use of the Internet is largely dependent upon the user's behaviour that, in turn, affects their attitudes. Even when remarkable opportunities exist for the deployment of technology, adverse attitude can inhibit use. Keeping this in mind, a survey of 166 academics of four technical and vocational colleges was conducted to study the attitudes of academics toward the use of the Internet. This study develops a model and validates two specific attitudinal variables--perceived usefulness and perceived ease of use--which are hypothesized to be fundamental determinants of use of the Internet. Adding two more variables--such as task characteristics and computer exposure--test the parsimony of the model further. A structural equation modelling technique is used to validate the model. The study confirmed that 79% of academics are using the Internet. Computer experience, perceived usefulness and perceived ease of use remain to be fundamental determinants of attitude formation. No other variables have been found to be significant.     

Negative knowledge toward a strategy for asking in logic programming

Rule-based systems that ask the user and that also allow a not operator to be used in the rules have existed for some time; see, for example, the work of Duda, Gasching, Hart, Konolige, Reboh, Barrett & Slocum (1978). This paper briefly explores an idea of Edmonds (1984) for bringing together such recent developments within logic programming in order to provide a logic-based system with an integral, automatic, strategy for asking. The discussion shows that a simple and natural interpretation of PROLOG can provide a step towards logic-based human-computer co-operation.     

智能体视觉策略在RoboCup中的设计

智能体对环境的认识是其进行决策的重要依据。在机器人足球仿真比赛(RoboCup)中,视觉感知是智能体获得信息、构建世界模型的主要途径。针对仿真比赛中球员智能体进行决策时信息不准确的问题,结合优先级和可信值对智能体的视觉策略进行了设计。根据对观察目标需求的紧迫程度动态地指定观察目标的优先级,然后结合世界模型维护的观察目标的可信值生成各个视觉角度的评价值,通过搜索评价值最大的视觉角度获得优化的视觉信息。实验结果表明这种视觉策略使球员动作的执行更加可靠,增强了球队的整体性能。     

Identifying factors that influence the acceptability of smart devices: implications for recommendations

This paper presents results from a web-based study that investigates users’ attitudes toward smart devices, focusing on acceptability. Specifically, we conducted a survey that elicits users’ ratings of devices in isolation and devices in the context of tasks potentially performed by these devices. Our study led to insights about users’ attitudes towards devices in isolation and in the context of tasks, and about the influence of demographic factors and factors pertaining to technical expertise and experience with devices on users’ attitudes. The insights about users’ attitudes provided the basis for two recommendation approaches based on principal components analysis (PCA) that alleviate the new-user and new-item problems: (1) employing latent features identified by PCA to predict ratings given by existing users to new devices, and by new users to existing devices; and (2) identifying a relatively small set of key questions on the basis of PCs, whose answers account to a large extent for new users’ ratings of devices in isolation and in the context of tasks. Our results show that taking into account latent features of devices, and asking a relatively small number of key questions about devices in the context of tasks, lead to rating predictions that are significantly more accurate than global and demographic predictions, and substantially reduce prediction error, eventually matching the performance of strong baselines.     

Identifying the underlying structure of demand during promotions: A structural equation modelling approach

It is widely recognized that promotions enhance product sales. Other factors such as seasonality, special days, customer preferences and economic factors may also have an impact on demand. Yet, endeavours to model the dependency of demand on various factors are scarce. We develop a conceptual demand-factor model to represent the dependency of demand on these factors. We examine and validate the conceptual model through structural equation modelling, using sales data of a leading soft drink company involved in supply chain collaboration with downstream retailers. We find that promotional and seasonal factors have a large and direct impact on sales, while special days like festivals and bank holidays have limited influence. Our approach is versatile and can assist demand planners in understanding the demand during promotional events, and in the planning and execution of promotional deals.     

Continuance use intention of enterprise instant messaging: a knowledge management perspective

Instant messaging has been widely utilised by a variety of types of organisations to improve the efficiency and effectiveness of internal communication and knowledge management (KM). To date, though, the continuance use of enterprise instant messaging (EIM) and its impact on KM outcomes have not been well understood in both academia and practice. This paper uses the decomposed theory of planned behaviour to explore factors that influence continual usage of EIM applications within organisations, as well as the impact of the continuance use decisions on knowledge creation, transfer, and retention within organisations. Our results are useful in developing guidelines and strategies to increase the likelihood of the continuance use of EIM and to improve the potential outcome of its use within organisations.     

Organizational and knowledge management related antecedents of knowledge use: the moderating effect of ambiguity tolerance

To leverage their knowledge resources, many organizations deploy knowledge management systems (KMS), which contain at their core a knowledge repository. Successful use of the knowledge/repository depends largely on knowledge workers who must contribute to the knowledge base as well as use those knowledge objects. We develop and validate an empirical model that explains mechanisms required to reduce knowledge gaps in a firm or business unit, through knowledge contribution and use or reuse. Using the theory of planned behavior and its derivative the technology acceptance model, we find that a feature rich user friendly KMS combined with extrinsic rewards (or incentives) and knowledge self-efficacy are critical to KM success. Tolerance of ambiguity (a personal trait) is a key influence in the causal relationship between incentives and knowledge gaps as well as knowledge self-efficacy and knowledge gaps. The main findings of this study are (1) for ambiguity intolerant individuals, incentives and self-efficacy are as important as the KMS as motivators for knowledge sharing and use, (2) for this group, it is necessary to stress the importance of KM as a means of social persuasion in building knowledge self-efficacy, (3) for the ambiguity tolerant, incentives are not required, and self efficacy is self-determined i.e., KM champions are not required to highlight the importance of KM; in fact the technology (KMS) is all important, a finding that negates conventional wisdom that technology is merely an enabler. In summary, a one-size fits all mindset is not effective, a firm must carefully consider the ambiguity tolerance levels of employees before forging ahead with KM efforts. This becomes all the more important to practitioners because of the recently highlighted differences in ambiguity tolerance between “Millennials” and” Generation Xers”. In addition to theory testing in KM, this research also informs KM practice.     

General and domain-specific influence of prior knowledge on setting of goals and content use in museum websites

This article explores the influence of prior knowledge on the setting of goals and use of content in museum website visits. Goal setting is a crucial process in organizing the activities of surfers in open environments, such as museum websites, where surfers faced ill-defined tasks. To this end, 12 graduate students were asked to surf through two museum websites in different domains (anthropology and natural sciences). Half of the participants had robust background in anthropology. Within each of these groups, half of the students had spent more than four years in graduate school, while the other half were first year students. Visited pages and concurrent comments were recorded, coded and analyzed. Results showed that domain specific knowledge influences goal setting process. Visitors with high domain knowledge tended to set larger and more consistent goal structures than first year students. Results showed also that general previous knowledge influences content use. Surfers with higher general skills tended to elaborate more on content and use more intensively the information available at the websites.     

Identifying short-range and long-range structural components of a compacted soil: an integrated geostatistical and spectral approach

Spectral analysis of geostatistical data has revealed the pore space of a compacted soil does not contain any specific structural component (laminations). Spectral analysis (using a discrete Fourier transform) of experimental variogram values revealed that a number of possible periodic components (laminations) did exist for individual vertical colour profiles (taken from images of the pore space captured using a scanning electron microscope). However, the periodicity varied laterally. The degree of lateral variation was found to depend on pore size. This suggests that the petrographic textures associated with compaction are not dominated by a small number of specific structural periodicities but a large number of localised structures. The analysis was carried out using a purpose built program called Specprofile.     

Fynesse: An architecture for integrating prior knowledge in autonomously learning agents

Reinforcement learning is an optimisation technique for applications like control or scheduling problems. It is used in learning situations, where success and failure of the system are the only training information. Unfortunately, we have to pay a price for this powerful ability: long training times and the instability of the learning process are not tolerable for industrial applications with large continuous state spaces. From our point of view, the integration of prior knowledge is a key mechanism for making autonomous learning practicable for industrial applications. The learning control architecture Fynesse provides a unified view onto the integration of prior control knowledge in the reinforcement learning framework. In this way, other approaches in this area can be embedded into Fynesse. The key features of Fynesse are (1) the integration of prior control knowledge like linear controllers, control characteristics or fuzzy controllers, (2) autonomous learning of control strategies and (3) the interpretation of learned strategies in terms of fuzzy control rules. The benefits and problems of different methods for the integration of a priori knowledge are demonstrated on empirical studies.The research project F ynesse was supported by the Deutsche Forschungsgemeinschaft (DFG).     

Unplanned effects of intelligent agents on Internet use: a social informatics approach

This paper instigates a discourse on the unplanned effects of intelligent agents in the context of their use on the Internet. By utilizing a social informatics framework as a lens of analysis, the study identifies several unanticipated consequences of using intelligent agents for information- and commerce-based tasks on the Internet. The effects include those that transpire over time at the organizational level, such as e-commerce transformation, operational encumbrance and security overload, as well as those that emerge on a cultural level, such as trust affliction, skills erosion, privacy attrition and social detachment. Furthermore, three types of impacts are identified: economic, policy, and social. The discussion contends that economic impacts occur on the organizational level, social effects transpire on a cultural level, and policy impacts take place on both levels. These effects of the use of intelligent agents have seldom been predicted and discussed by visionaries, researchers, and practitioners in the field. The knowledge of these unplanned outcomes can improve our understanding of the overall impacts that innovative agent technologies may potentially have on organizations and individuals. Subsequently, this may help us develop better agent applications, facilitate the formulation of appropriate contingencies, and provide impetus for future research.