arXiv subjects

Besat Kassaie

Publications and source records attributed to Besat Kassaie.

6 recordsLinked to original sources

arXiv2026

Novel Table Search [Technical Report]

Avoiding redundancy in query results has been extensively studied in relational databases and information retrieval, yet its implications for data lakes remain largely unexplored. We bridge this gap by investigating how to discover unionable tables that contribute new information for a given query table in large-scale data lakes. We formally define Novel Table Search (NTS) as the problem of finding tables that are novel with respect to a given query table and identify two desirable properties that any scoring function for NTS should satisfy. We introduce a concrete scoring mechanism designed to maximize syntactic novelty, prove that it satisfies the proposed properties, and show that the associated optimization problem is NP-hard. To address this challenge, we develop an efficient approximation technique based on penalization, i.e., Attribute-Based Novel Table Search (ANTs). We propose three additional NTS variants to achieve syntactic novelty and introduce two evaluation metrics for syntactic novelty. Through extensive experiments, we demonstrate that ANTs outperforms other methods in capturing syntactic novelty across evaluation metrics and various benchmarks, while also achieving the lowest execution time.

cs.DB↗

arXiv2025

Improving Unstructured Data Quality via Updatable Extracted Views

Improving data quality in unstructured documents is a long-standing challenge. Unstructured data, especially in textual form, inherently lacks defined semantics, which poses significant challenges for effective processing and for ensuring data quality. We propose leveraging information extraction algorithms to design, apply, and explain data cleaning processes for documents. Specifically, for a simple document update model, we identify and verify a set of sufficient conditions for rule-based extraction programs to qualify for inclusion in our document cleaning framework. Through experiments conducted on medical records, we demonstrate that our approach provides an effective framework for identifying and correcting data quality problems, thereby highlighting its practical value in real-world applications.

cs.DB↗

arXiv2020

Detecting Opportunities for Differential Maintenance of Extracted Views

Semi-structured and unstructured data management is challenging, but many of the problems encountered are analogous to problems already addressed in the relational context. In the area of information extraction, for example, the shift from engineering ad hoc, application-specific extraction rules towards using expressive languages such as CPSL and AQL creates opportunities to propose solutions that can be applied to a wide range of extraction programs. In this work, we focus on extracted view maintenance, a problem that is well-motivated and thoroughly addressed in the relational setting. In particular, we formalize and address the problem of keeping extracted relations consistent with source documents that can be arbitrarily updated. We formally characterize three classes of document updates, namely those that are irrelevant, autonomously computable, and pseudo-irrelevant with respect to a given extractor. Finally, we propose algorithms to detect pseudo-irrelevant document updates with respect to extractors that are expressed as document spanners, a model of information extraction inspired by SystemT.

cs.DB↗

arXiv2017

SPARQL over GraphX

The ability of the RDF data model to link data from heterogeneous domains has led to an explosive growth of RDF data. So, evaluating SPARQL queries over large RDF data has been crucial for the semantic web community. However, due to the graph nature of RDF data, evaluating SPARQL queries in relational databases and common data-parallel systems needs a lot of joins and is inefficient. On the other hand, the enormity of datasets that are graph in nature such as social network data, has led the database community to develop graph-parallel processing systems to support iterative graph computations efficiently. In this work we take advantage of the graph representation of RDF data and exploit GraphX, a new graph processing system based on Spark. We propose a subgraph matching algorithm, compatible with the GraphX programming model to evaluate SPARQL queries. Some experiments are performed to show the system scalability to handle large datasets.

cs.DB↗

arXiv2017

Job Detection in Twitter

In this report, we propose a new application for twitter data called \textit{job detection}. We identify people's job category based on their tweets. As a preliminary work, we limited our task to identify only IT workers from other job holders. We have used and compared both simple bag of words model and a document representation based on Skip-gram model. Our results show that the model based on Skip-gram, achieves a 76\% precision and 82\% recall.

cs.CL↗

arXiv2017

De-identification In practice

We report our effort to identify the sensitive information, subset of data items listed by HIPAA (Health Insurance Portability and Accountability), from medical text using the recent advances in natural language processing and machine learning techniques. We represent the words with high dimensional continuous vectors learned by a variant of Word2Vec called Continous Bag Of Words (CBOW). We feed the word vectors into a simple neural network with a Long Short-Term Memory (LSTM) architecture. Without any attempts to extract manually crafted features and considering that our medical dataset is too small to be fed into neural network, we obtained promising results. The results thrilled us to think about the larger scale of the project with precise parameter tuning and other possible improvements.

cs.CL↗