• Journal of Information Processing Systems
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• v.16 no.4
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• pp.859-869
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• 2020

This paper introduces page-level rewrite interval prediction (PRWIP). By recording and analyzing the memory access history at page-level, we are able to predict the future memory accesses to any pages. Leveraging this information, this paper proposes a faster incremental checkpoint design by overlapping checkpoint phase with computing phase and thus achieves higher performance. Experimental results show that our new incremental checkpoint design can achieve averagely 22% speedup over traditional incremental checkpoint and 14% over the previous state-of-the-art work.

• The Journal of Industrial Distribution & Business
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• v.5 no.1
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• pp.5-9
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• 2014

Purpose - This study aims to examine the incremental information content of cash flw from operations and earnings in the Iranian capital market. Design, methodology, and approach - Based on market-based accounting research, this study uses statistical associations between accounting data (earnings and cash flw) and stock returns to assess/measure the incremental information content (value relevance) of cash flw and earnings. A multivariate regression model based on panel data is used to examine the incremental information content of earnings and cash flow from operations. Results - The results show that both earnings and cash flow from operations have incremental information content beyond each other. These results are consistent with the findings of recent studies. Overall, the fidings of this study support the usefulness of cash flw information in addition to earnings, in fim valuation by investors in the Iranian market. Conclusions - The study makes the following contributions to the Iranian literature on incremental information content of cash flw and earnings. First, this study employs actual cash flw data derived from cash flw statements. Second, this study employs a large sample size for a more recent period.

• Journal of Internet Computing and Services
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• v.7 no.4
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• pp.91-99
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• 2006

The incremental parsing technique plays an important role in language-based environment which allows the incremental construction of a program. It improves the performance of a system by reanalyzing only the changed part of a program. The conventional incremental parsing uses the stack data structure in order to store the parsing information. In this paper, we suggest a threaded tree construction algorithm which parse by adding the threaded node address instead of using a stack data structure. We also suggest an incremental threaded tree construction which has incremental parsing process of five steps using the constructed threaded tree.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.6
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• pp.861-870
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• 1990

A method of recognizing objects is proposed that uses a concept of modified incremental circle transform. The modified incremental circle transform, which maps bundaries of an object into an unit circle, represnets efficiently the shape of the boundaries detected in digitized binary images of the objects. It is proved that modified incremental circle transform of object, which is invariant under object translation, rotation, and size, can be used as feature information for recognizing two dimensional polygonal object efficiently.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.8
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• pp.1010-1018
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• 1988

This paper proposes a MOS circuit extractor which extracts a netlist from the hierarchical mask information, for the verification tools. To utilize the regularity and the simple representation of the hierarchical circuit, and to reduce the debug cycle of design, verification, and modification, we propose a hierarvhical and incremental circuit extraction algorithm. In flat circuit extraction stage, the multiple storage quad tree is used as an internal data structure. Incremental circuit extraction using the hierarchical structure is made possible, to reduce the re-extraction time of the modified circuit.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2567-2582
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• 2016

This paper investigates the impact of outdated channel state information (CSI) on the performance of the incremental amplify-and-forward (AF) opportunistic relaying (OR) over dual-hop Rayleigh fading channels. According to the definition of distribution function, we obtain the cumulative distribution function (CDF) of the actual combined signal-to-noise ratio (SNR) received at the destination. Based on this CDF, the closed-form expressions of the average spectral efficiency and outage probability are derived for the incremental AF OR under outdated CSI. Numerical results show that in the low region of the average SNR of the direct link, outdated CSI deteriorates the system performance, whereas in the high region, outdated CSI has almost no impact on the system performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.9-17
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• 1994

The incremental parsing techniques are essential part of language-based environment which allow incremental construction of programs, as reparsing of modified part of the program alone enhances the system performance. A number of methods and algorithms have been proposed for incremental parsing and for the construction of incremental parsers. The purpose of this paper is to review the earlier incremental parsing algorithms which are too expensive in both time and space, and to present an efficient incremental LR parsing algorithm which is more efficient than the previous ones. In this paper, we generate automatically an extended LR parsing tables which allow the nonterminal symbols as well as terminal symbols for the input, and apply them to our efficient incremental LR parsing algorithm. Using these extended LR parsing tables. we suggest several methods to reduce its memory spaces and parsing steps as well. The algorithms described here were implemented in C language and YACC on a SUN UNIX OS, and were tested with several grammars and strings.

• Journal of Information Processing Systems
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• v.6 no.2
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• pp.163-176
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• 2010

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.151-158
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• 2000

This dissertation measures transitory items using earnings change scaled by beginning-of-period price(FreemanㆍTse 1992) and the earnings-to-price ratio(AliㆍZarowin 1992). Contextual regression model results confirm the incremental explanatory power for predominantly permanent earnings, and suggest that cash flows also have incremental explanatory power in the presence of predominantly permanent earnings. But contextual regression results represent that while earnings are consistent with a smaller marginal impact from extreme (transitory) earnings on abnormal returns, cash flows have no greater impact on abnormal returns in the presence of large transitory components in earnings.