Discovering Program Topoi via Hierarchical Agglomerative Clustering

Ieva, Carlo; Gotlieb, Arnaud; Kaci, Souhila; Lazaar, Nadjib

doi:10.1109/tr.2018.2828135

Cited by 12 publications

(4 citation statements)

References 33 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The divisive approaches start from the whole graph and gradually split it into smaller clusters using edge cuts or hyperplanes until the clusters are formed [19], [20], [21], [22]. The agglomerative approaches build clusters by iteratively merging the most correlated nodes or subgraphs until the clusters are formed [23], [24], [25]. A common challenge these approaches face is the efficiency issue, since analyzing a large-scale graph node-bynode is a time-consuming process.…”

Section: Graph Clusteringmentioning

confidence: 99%

“…The Amoeba algorithm splits these attributes mainly based on their values. For example, given a value range [0, 100], the Amoeba algorithm might consolidate them into 4 blocks with 4 value ranges: [0, 25), [25,50), [50,75), [75,100]. However, this schema may not be the optimal schema since the value could be accessed differently.…”

Section: On Turbine Log Data Setmentioning

confidence: 99%

See 1 more Smart Citation

Consolidating Industrial Small Files Using Robust Graph Clustering

Wang

Lan

Peng

et al. 2023

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

Small file management is widely encountered in industrial areas. Consolidating small files can benefit the performance of the data management system. Many existing consolidation solutions fail to realize the importance of a proper consolidation schema. Therefore, they use very primitive and ineffective schemas. In this paper, we focus on proposing an effective and robust consolidation schema. Unlike most of the existing solutions that only focus on the historical workload, we consider the workload uncertainty issue and propose a graph-clustering-based solution that is more robust to workload uncertainty in the future. To do this, we introduce robust optimization, a mathematical model that provides theoretical support for solving uncertainty issues. Then, we demonstrate that the robustness of the consolidation schema can be achieved using a graph clustering algorithm with a duplication mechanism. Since duplication leads to data redundancy, we propose a parameter to control the redundancy and the robustness of the schema. We also propose two algorithms to estimate the parameter automatically. Experimental results on both the synthetic and real-life data sets show the effectiveness of our algorithm.

show abstract

Section: Graph Clusteringmentioning

confidence: 99%

Section: On Turbine Log Data Setmentioning

confidence: 99%

Consolidating Industrial Small Files Using Robust Graph Clustering

Wang

Lan

Peng

et al. 2023

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…However, due to the reduction of data annotation, the performance of the model was unsatisfactory. Through clustering and entry point selection, Ieva, C et al [18] proposed a method to automatically extract the main functions of the program and carried out feature extraction and tracking. Park, BK et al [19] proposed a linguistic analysis method based on the semantic analysis of the Fillmore's textual approach.…”

Section: Related Workmentioning

confidence: 99%

BiLSTM-CRF-KG: A Construction Method of Software Requirements Specification Graph

Wang

Pan

Chen³

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

A requirement analysis is the basis and source of software system development, and its accuracy, consistency and completeness are the keys to determining software quality. However, at present, most software requirements specifications are prepared manually, which has some problems, such as inconsistency with business description, low preparation efficiency, being error prone and difficulty communicating effectively with business personnel. Aiming at the above problems, this paper realizes a construction model of the software requirements specification graph BiSLTM-CRF-KG by using natural language processing and knowledge graph technology. Simulation experiments on 150 real software system business requirements description corpora show that the BiSLTM-CRF-KG model can obtain 96.31% functional entity recognition accuracy directly from the original corpus, which is better than the classical BiSLTM-CRF, IDCNN-CRF and CRF++ models, and has good performance on different kinds of data sets.

show abstract

“…HAC has made significant studies in the theoretical community and in the application by practitioners. See the related papers (Gan, Wei & Johnstone, 2015;Xu et al 2016;Tie, et al 2018;Ieva et al 2019).…”

Section: Grouping Of Data Objects or Document Representationsmentioning

confidence: 99%

A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information

Azeroual¹

2019

CIS

View full text Add to dashboard Cite

With the increased accessibility of research information, the demands on research information systems (RIS) that are expected to automatically generate and process knowledge are increasing. Furthermore, the quality of the RIS data entries of the individual sources of information causes problems. If the data is structured in RIS, users can read and filter out their information and knowledge needs without any problems. This technique, which nevertheless allows text databases and text sources to be analyzed and knowledge extracted from unknown texts, is referred to as text mining or text data mining based on the principles of data mining. Text mining allows automatically classifying large heterogeneous sources of research information and assigning them to specific topics. Research information has always played a major role in higher education and academic institutions, although they were usually available in unstructured form in RIS and grow faster than structured data. This can be a waste of time searching for RIS staff in universities and can lead to bad decision-making. For this reason, the present paper proposes a new approach to obtaining structured research information from heterogeneous information systems. It is a subset of an approach to the semantic integration of unstructured data using the example of a RIS. The purpose of this paper is to investigate text and data mining methods in the context of RIS and to develop an improvement quality model as an aid to RIS using universities and academic institutions to enrich unstructured research information.

show abstract

Discovering Program Topoi via Hierarchical Agglomerative Clustering

Cited by 12 publications

References 33 publications

Consolidating Industrial Small Files Using Robust Graph Clustering

Consolidating Industrial Small Files Using Robust Graph Clustering

BiLSTM-CRF-KG: A Construction Method of Software Requirements Specification Graph

A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information

Contact Info

Product

Resources

About