SPATIO-TEMPORAL

• Minkyu Kim and Suan Lee
• Big Data and Smart Computing (BigComp), 2021 IEEE International Conference on. IEEE, 2021.
• January 17-20, 2021

Abstract

Those who want to start their own businesses must decide a location and service to start. In order to make the decision, they must know characteristics of the location and service, such as average revenues and floating population. However, it is usually very difficult to collect and analyze these characteristics. Therefore, we propose a novel deep learning model named Neural Tensor Factorization (NeuralTF) that automatically analyzes the characteristics for predicting revenues, and a method for recommending appropriate location or service to start their businesses based on the predicted revenues. NeuralTF is a combination of Tensor Factorization(TF) and Deep Neural Network(DNN). We compare NeuralTF with other machine learning models using Seoul Commercial Alley dataset. In addition, we compare performances of NeuralTF when TF and DNN components share the embedding space and when they do not.

Keywords

Recommender System, Tensor Factorization, Neural Network, Deep Learning, Neural Tensor Factorization

• Minkyu Kim, Suan Lee, and Jinho Kim
• 2020 IEEE International Conference on Big Data (IEEE BigData 2020)
• December 10-13, 2020

Abstract

Regression analysis is one of the most widely used data analysis methods, and it is increasingly important to obtain accurate results from it. To obtain accurate prediction results of regression analysis through machine learning, we must select appropriate features and train various feature interactions. The combinatorial model consists of a combination of various subordinate components and is used for automatic training of various feature interactions. However, existing combinatorial models are inefficient b ecause t hey c an t rain o nly limited feature interactions and must combine several components. To overcome these limitations, this study proposes a new model called eXtreme Interaction Network (XIN). XIN can automatically learn various explicit interactions, various levels of implicit higher-order interactions, and polynomial features. We compared the proposed XIN with existing models using four datasets with different characteristics to demonstrate that the proposed model has higher performance and lower or comparable time and space complexities. Furthermore, we conducted experiments while changing the various hyper-parameters of the XIN and demonstrated the improved performance of the proposed method in various environments.

Keywords

Neural Networks, Deep Learning, Cross Network, Combinatorial Model, Feature Interactions

• Minkyu Kim, Suan Lee, and Jinho Kim
• Big Data and Smart Computing (BigComp)
• February 19-22, 2020

Abstract

Wide & Deep model is deep learning model that jointed between of wide component and deep component for regression, recommendation, and classification. However, there is no study of regression analysis using Wide & Deep model. Also, Wide component of Wide & Deep model only deal with categorical variables and that need hand-crafted variables for efficient training. Therefore, this paper propose Lattice Wide & Deep Architecture which improve Wide & Deep model. Furthermore we show that the Lattice Wide & Deep model has better performance than Wide & Deep model in regression analysis.

Keywords

lattice wide & deep, wide & deep learning, regression analysis

• SungJin Park, Suan Lee, and Jinho Kim
• Big Data and Smart Computing (BigComp)
• February 19-22, 2020

Abstract

As for people who wish to start their own businesses, their concerns are whether they could survive during their operations because most stores or services in Seoul are not able to survive for more than 5 year possibly due to the poor decision of location/service to start. In order to solve this problem, using big data could be helpful to increase the survival rate. Singular Value Decomposition (SVD) has been widely used in finding the similarity between all pairs of alleys and obtaining predictions from unknown relevance scores. Since tensor decomposition is the extension of SVD for multi-dimensional data, using this method to find the similarity between all pair of alleys could be the solution of increasing survival rate. This paper aims to generate good prediction tensor, TENSORCABS, that is able to recommend users appropriate alley location to start their businesses or the appropriate services to start at the user’s desired location. Both CP and Tucker decompositions are used and compared to evaluate which method has better performance. Also, r-square for regression problem and precision & recall for top-k recommendation performance are used to evaluate the TENSORCABS. As results, actual and predicted values are good-fitted, and prediction tensor performs well on the top-k recommendation. In addition, Tucker outperforms CP for this situation. Therefore, the proposed method has advantages that can handle high-dimensional data and can use decomposition for recommendations of various perspectives. With this method, users are able to obtain recommendations of the appropriate alleys with predicted revenues for opening any business service or the appropriate services with predicted revenues on a user’s desired alley location.

Keywords

Recommendation System, Tucker Decomposition, Canonical Polyadic, Collaborative Filtering, Top-k Recommendation, Tensor, Commercial Alley

• Suan Lee, Sunhwa Jo, and Jinho Kim
• Big Data and Smart Computing (BigComp), 2015 International Conference on. IEEE
• 9-11 Feb. 2015

Abstract

Data cube is used as an OLAP (On-Line Analytical Processing) model to implement multidimensional analyses in many fields of application. Computing a data cube requires a long sequence of basic operations and storage costs. Exponentially accumulating amounts of data have reached a magnitude that overwhelms the processing capacities of single computers. In this paper, we implement a large-scale data cube computation based on distributed parallel computing using the MapReduce (MR) computational framework. For this purpose, we developed a new algorithm, MRDataCube, which incorporates the MR mechanism into data cube computations such that effective data cube computations are enabled even when using the same computing resources. The proposed MRDataCube consists of two-level MR phases, namely, MRSpread and MRAssemble. The main feature of this algorithm is a continuous data reduction through the combination of partial cuboids and partial cells that are emitted when the computation undergoes these two phases. From the experimental results we revealed that MRDataCube outperforms all other algorithms.

Keywords

distributed parallel algorithm, cube, OLAP, multi-dimensional analysis, data cube computation, MapReduce, Hadoop

• Suan Lee, Namsoo Kim, and Jinho Kim
• Big Data and Cloud Computing (BdCloud), 2014 IEEE Fourth International Conference on. IEEE
• 3-5 Dec. 2014

Abstract

Recently, unstructured data like texts, documents, or SNS messages has been increasingly being used in many applications, rather than structured data consisting of simple numbers or characters. Thus it becomes more important to analysis unstructured text data to extract valuable information for usres decision making. Like OLAP (On-Line Analytical Processing) analysis over structured data, Multi-dimensional analysis for these unstructured data is popularly being required. To facilitate these analysis requirements on the unstructured data, a text cube model on multi-dimensional text database has been proposed. In this paper, we extended the existing text cube model to incorporate TF-IDF (Term Frequency Inverse Document Frequrency) and LM (Language Model) as measurements. Because the proposed text cube model utilizes new measurements which are more popular in information retrieval systems, it is more efficient and effective to analysis text databases. Through experiments, we revealed that the performance and the effectiveness of the proposed text cube outperform the existing one.

Keywords

language model, OLAP, Multi-dimensional analysis, text cube, data cube, text databases, information retrieval, TF-IDF

• Jinho Kim, Suan Lee, Ji-Seop Won, and Yang-Sae Moon
• Dependable, Autonomic and Secure Computing (DASC), 2011 IEEE Ninth International Conference on. IEEE
• 12-14 Dec. 2011

Abstract

Recently new mobile devices such as cellular phones, smart phones, and digital cameras are popularly used to take photos. By the virtue of these convenient instruments, we can take many photos easily, but we suffer from the difficulty of managing and searching photos due to their large volume. This paper develops a mobile application software, called Photo Cube, which automatically extracts various metadata for photos (e.g., date/time, place/address, weather, personal event, etc.) by taking advantage of sensors and networking functions embedded in mobile smart phones like Android phones or iPhones. The metadata can be used to manage and to search photos. Using this Photo Cube, users will be able to classify, store, manage, and search a large number of photos easily, without specifying any information but just clicking the shutter in a camera. The Photo Cube system was implemented on smart phones using Google's Android.

Keywords

multidimensional search, photo metadata, photo annotation, image databases, mobile application, smartphones, text search