• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.141-152
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• 2002

Data dictionary is a collection of meta-data, which describes data produced and consumed while performing business processes. Data dictionary is an essential element for business process standardization and automation, and has a fundamental role in ERP application management and customization. Also, data dictionary facilitates B2B processes by enabling painless integration of business processes between various enterprises. We implemented data dictionary support in SEA＋, a component- based scalable ERP system developed in ETRI, and found out that it's a plausible feature of business information system. We discovered that data dictionary promotes semantic, not syntactic, data management, which can make it possible to leverage viability of the tool in the coming age of more meta-data oriented computing world. We envision that business data dictionary is a firm foundation of adapting business knowledge, applications and processes into the semantic web based enterprise infra-structure.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.483-491
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• 2001

Data dictionary is a collection of metadata about data defined, produced and consumed while performing business processes. Data dictionary is an essential element for business process standardization and automation. Data dictionary also has a fundamental role in ERP application management and customization. Finally, data dictionary helps B2B by gracefully integrating intra-enterprise business processes and inter-enterprise business processes. This paper gives some clues about the importance of data dictionary in ERP and B2B, and introduces data dictionary support of SEA+, a component-based scalable ERP package system.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.37-52
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• 2003

ISO 13584 Parts Library (PLIB) standard is making its way into e-business as a norm for classifying products and their characteristics. PLIB is a multi-parts standard, and the Part 42: Methodology for structuring Parts families Provides the information model and design Principles for the data dictionary of parts library or e-catalog. If e-catalog systems are built using a data dictionary that is constructed based on PLIB dictionary data model, many different e-catalog systems can be easily integrated and interoperated. This paper studies the roles and requirements of the data dictionary in e-catalog, and applies the data model and design principles of PLIB Part 42 to construct a data dictionary from the viewpoint of ontology Based on the analysis results, we propose a data dictionary of die and mold parts, and implementat the B2B e-catalog system.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.587-592
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• 2001

Many applications are being developed to automate office works, and the electronic dictionary(e-Dictionary) is one of the main components of the office suites. Several requirements are proposed for the efficient e-dictionaries :1) Fast searching time, 2) Data compatibility with other e-dictionaries to deal with words and obsolete word, and 3) Reusable components to develop new customized e-dictionaries with minimized development time and cost. We propose a data format with which any e-dictionary can change data with others. We also develop System Dictionary component and Customer Dictionary component to enable-and-play component reuse. Our e-dictionary achieves fast searching time by efficiently managing Trie and B-tree index structure for the dictionary components.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.481-486
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• 2023

Anomaly detection holds paramount significance across diverse fields, encompassing fraud detection, risk mitigation, and sensor evaluation tests. Its pertinence extends notably to the military, particularly within the Warrior Platform, a comprehensive combat equipment system with wearable sensors. Hence, we propose a data-compression-based anomaly detection approach tailored to unlabeled time series and sequence data. This method entailed the construction of two distinctive features, typicality and atypicality, to discern anomalies effectively. The typicality of a test sequence was determined by evaluating the compression efficacy achieved through the pattern dictionary. This dictionary was established based on the frequency of all patterns identified in a training sequence generated for each sensor within Warrior Platform. The resulting typicality served as an anomaly score, facilitating the identification of anomalous data using a predetermined threshold. To improve the performance of the pattern dictionary method, we leveraged atypicality to discern sequences that could undergo compression independently without relying on the pattern dictionary. Consequently, our refined approach integrated both typicality and atypicality, augmenting the effectiveness of the pattern dictionary method. Our proposed method exhibited heightened capability in detecting a spectrum of unpredictable anomalies, fortifying the stability of wearable sensors prevalent in military equipment, including the Army TIGER 4.0 system.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.101-109
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• 2013

Data quality management is extremely important. In this study, we proposed data standardization for effective quality management of enterprise-owned data about distributed goods and validated its effectiveness by case study. For the standardization of data, we designed data category and data dictionary. Additionally, we categorized data and identified its attributes for data category design, and we developed design process for data dictionary and built the dictionary of word, term, domain and code for data dictionary design. And then we proposed output documents which have to be written for data standardization. Proposed data standardization approach is validated its efficiency by quantitative and qualitative measurement. and as a result the data quality of the data standardization improved 24% and the data quality of the consistency of the data dictionary improved 7%.

• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.127-142
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• 2001

For the B2B e-commerce and SCM (Supply Chain Management), standardization of electronic catalogue that contains product and business data is important. Especially, standardization of hierarchies that is required for categorization, and standardization of product properties are difficult and costly to maintain. The ability of searching for items and data in databases is critical for successful e-commerce system. This paper introduces the data dictionary of PUB (ISO 13584) part 42 which can establish the standard of product data. The method is applied to develop the data dictionary of automotive parts.

• Smart Structures and Systems
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• v.25 no.2
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• pp.123-133
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• 2020

Compressive sensing (CS) is a newly developed data acquisition and processing technique that takes advantage of the sparse structure in signals. Normally signals in their primitive space or format are reconstructed from their compressed measurements for further treatments, such as modal analysis for vibration data. This approach causes problems such as leakage, loss of fidelity, etc., and the computation of reconstruction itself is costly as well. Therefore, it is appealing to directly work on the compressed data without prior reconstruction of the original data. In this paper, a direct approach for modal analysis of damped systems is proposed by decomposing the compressed measurements with an appropriate dictionary. The damped free vibration function is adopted to form atoms in the dictionary for the following sparse decomposition. Compared with the normally used Fourier bases, the damped free vibration function spans a space with both the frequency and damping as the control variables. In order to efficiently search the enormous two-dimension dictionary with frequency and damping as variables, a two-step strategy is implemented combined with the Orthogonal Matching Pursuit (OMP) to determine the optimal atom in the dictionary, which greatly reduces the computation of the sparse decomposition. The performance of the proposed method is demonstrated by a numerical and an experimental example, and advantages of the method are revealed by comparison with another such kind method using POD technique.

• Speech Sciences
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• v.8 no.1
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• pp.33-52
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• 2001

A bilingual sound dictionary in Korean and English has been created for a broad range of sound reference to cross-linguistic, dialectal, native language (L1)-transferred biological and allophonic variations. The paper demonstrates that the pronunciation dictionary of the lexicon is inadequate for sound reference due to the preponderance of unmarked sounds. The audio registry consists of the three-way comparison of 1) English speech from native English speakers, 2) Korean speech from Korean speakers, and 3) English speech from Korean speakers. Several sub-dictionaries have been created as the foundation research for independent development. They are 1) a pronunciation dictionary of the Korean lexicon in a keyboard-compatible phonetic transcription, 2) a sound dictionary of L1-interfered language, and 3) an audible dictionary of Korean sounds. The dictionary was designed to facilitate the exchange of the speech signal and its corresponding text data on various media particularly on CD-ROM. The methodology and findings of the construction are discussed.