Personalized query techniques in graphs: A survey

Lin, Pei Yu; Li, Yangfan; Luo, Wensheng; Zhou, Xu; Zeng, Yuanyuan; Li, Kenli; Li, Keqin

doi:10.1016/j.ins.2022.06.023

Cited by 5 publications

(4 citation statements)

References 78 publications

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“…The query model design was crucial in order to ensure periodic matching between digital and physical data [40]. The dataset comprised a management database containing heirlooms stored in museums and land and rice fields spread across several areas [41].…”

Section: Methodsmentioning

confidence: 99%

Query Model Framework Design for Conservation History and Endowments Database: A Case Study on the Digitization of the Sumedang Larang Kingdom’s History and Endowments in Indonesia

Sudrajat,

Ruchjana,

Abdullah

et al. 2023

Heritage

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The use of structured query formulation languages is a method for gaining access to data in information management systems. Writing structured queries is a powerful method of data retrieval, and end users can generate complex database queries by becoming proficient in a particular query language. Digitization of database information systems, history and endowment, and technology conservation are some examples of database conservation techniques in the process of displaying data that require optimal query techniques. Historical and endowment properties comply with a certain set of laws governing waqf recipients, which sets them apart from heritage and cultural assets. Trusted property typically comprises real estate, valuables, or both and has an infinite preservation term so long as it may still be put to use. A solid information technology infrastructure is essential for ensuring comprehensive data security. Given the availability of knowledge and the quick development of information technology, ensuring its sustainability is a challenge. This research is aimed at preserving historical and waqf databases based on the case study of the Kingdom of Sumedang Larang (KSL), Indonesia, through examination of the database, synchronization of digital data with physical data, as well as looking at its relationship with organizations and management. The proposed method, namely the Design and Analysis of Database Query Model for Preservation Information System (DAQMP), creates a recurring query model from the original tabular data placed in the next table as a special table check for data specifications containing important data. The results must be accurately aligned between digital and physical records regularly to serve as a record of any changes and as evidence to determine whether data has been corrupted, altered, or lost.

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Section: Methodsmentioning

confidence: 99%

Query Model Framework Design for Conservation History and Endowments Database: A Case Study on the Digitization of the Sumedang Larang Kingdom’s History and Endowments in Indonesia

Sudrajat,

Ruchjana,

Abdullah

et al. 2023

Heritage

View full text Add to dashboard Cite

show abstract

“…or to help the system make choices (Gauch et al, 2007;Sowbhagya et al, 2022). Personalization of the information access process consists of integrating or exploiting the user profile in the information access chain (Lin et al, 2022). Its fundamental goal is to restore, at the top of the list of results, documents that interest the user in their search, in other words, which seem most similar to their profile.…”

Section: Personalized Information Access and User Profile Modeling: R...mentioning

confidence: 99%

Learning-Based Models for Building User Profiles for Personalized Information Access

Sassi Hidri

2024

IJIKM

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Aim/Purpose: This study aims to evaluate the success of deep learning in building user profiles for personalized information access. Background: To better express document content and information during the matching phase of the information retrieval (IR) process, deep learning architectures could potentially offer a feasible and optimal alternative to user profile building for personalized information access. Methodology: This study uses deep learning-based models to deduce the domain of the document deemed implicitly relevant by a user that corresponds to their center of interest, and then used predicted domain by the best given architecture with user’s characteristics to predict other centers of interest. Contribution: This study contributes to the literature by considering the difference in vocabulary used to express document content and information needs. Users are integrated into all research phases in order to provide them with relevant information adapted to their context and their preferences meeting their precise needs. To better express document content and information during this phase, deep learning models are employed to learn complex representations of documents and queries. These models can capture hierarchical, sequential, or attention-based patterns in textual data. Findings: The results show that deep learning models were highly effective for building user profiles for personalized information access since they leveraged the power of neural networks in analyzing and understanding complex patterns in user behavior, preferences, and user interactions. Recommendations for Practitioners: Building effective user profiles for personalized information access is an ongoing process that requires a combination of technology, user engagement, and a commitment to privacy and security. Recommendation for Researchers: Researchers involved in building user profiles for personalized information access play a crucial role in advancing the field and developing more innovative deep-based networks solutions by exploring novel data sources, such as biometric data, sentiment analysis, or physiological signals, to enhance user profiles. They can investigate the integration of multimodal data for a more comprehensive understanding of user preferences. Impact on Society: The proposed models can provide companies with an alternative and sophisticated recommendation system to foster progress in building user profiles by analyzing complex user behavior, preferences, and interactions, leading to more effective and dynamic content suggestions. Future Research: The development of user profile evolution models and their integration into a personalized information search system may be confronted with other problems such as the interpretability and transparency of the learning-based models. Developing interpretable machine learning techniques and visualization tools to explain how user profiles are constructed and used for personalized information access seems necessary to us as a future extension of our work.

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“…A probabilistic reverse top-k queries for monochromatic and bichromatic cases over uncertain databases are proposed in 26 with effective pruning heuristics to reduce the search space. A comprehensive survey on personalized graph queries to compute personalized query results for users on the basis of their personalized preferences is presented in 27 . A scheme is developed to answer multidimensional range queries on multidimensional data using bucketization in 28 .…”

Section: Related Workmentioning

confidence: 99%

“…where Figure 1a shows a converted TMA of size 3 to C2A of size 27 . We describe the C2A with odd dimensions as row and even dimensions as column.…”

Section: Dimension Transformationmentioning

confidence: 99%

Multidimensional query processing algorithm by dimension transformation

Rimi

Hasan

Tsuji

2023

Sci Rep

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Multidimensional query processing is an important access pattern for multidimensional scientific data. We propose an in-memory multidimensional query processing algorithm for dense data using a higher-dimensional array. We developed a new array system namely a Converted two-dimensional Array (C2A) of a multidimensional array of dimension n ($$n > 2$$ n > 2 ) where the n dimensions are transformed into 2 dimensions. Using the C2A, we design and analyze less complex algorithms that show improve performance for data locality and cache miss rate. Therefore, improved performance for data retrieval is achieved. We demonstrate algorithms for single key and range key queries for both Traditional Multidimensional Array(TMA) and C2A. We also compare the performance of both schemes. The cost of index computation gets high when the number of dimensions increases in a TMA but the proposed C2A based algorithm shows less computation cost. The cache miss rate is also lower for in C2A based algorithm than TMA based algorithm. Theoretical and experimental results show that the performance of C2A based algorithm outperforms the TMA-based algorithms.

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Personalized query techniques in graphs: A survey

Cited by 5 publications

References 78 publications

Query Model Framework Design for Conservation History and Endowments Database: A Case Study on the Digitization of the Sumedang Larang Kingdom’s History and Endowments in Indonesia

Query Model Framework Design for Conservation History and Endowments Database: A Case Study on the Digitization of the Sumedang Larang Kingdom’s History and Endowments in Indonesia

Learning-Based Models for Building User Profiles for Personalized Information Access

Multidimensional query processing algorithm by dimension transformation

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