Query Terms Research Articles

A robust phenotype-driven likelihood ratio analysis approach assisting interpretable clinical diagnosis of rare diseases

Phenotype-based prioritization of candidate genes and diseases has become a well-established approach for multi-omics diagnostics of rare diseases. Most current algorithms exploit semantic analysis and probabilistic statistics based on Human Phenotype Ontology and are commonly superior to naive search methods. However, these algorithms are mostly less interpretable and do not perform well in real clinical scenarios due to noise and imprecision of query terms, and the fact that individuals may not display all phenotypes of the disease they belong to. We present a Phenotype-driven Likelihood Ratio analysis approach (PheLR) assisting interpretable clinical diagnosis of rare diseases. With a likelihood ratio paradigm, PheLR estimates the posterior probability of candidate diseases and how much a phenotypic feature contributes to the prioritization result. Benchmarked using simulated and realistic patients, PheLR shows significant advantages over current approaches and is robust to noise and inaccuracy. To facilitate clinical practice and visualized differential diagnosis, PheLR is implemented as an online web tool (https://phelr.nbscn.org).

Many are Better than One: Algorithm Selection for Faster Top-K Retrieval

Large-scale search engines have become a fundamental tool to efficiently access information on the Web. Typically, users expect answers in sub-second time frames, which demands highly efficient algorithms to traverse the data structures to return the top-k results. Despite different top-k algorithms that avoid processing all postings for all query terms, finding one algorithm that performs the fastest on any query is not always possible. The fastest average algorithm does not necessarily perform the best on all queries when evaluated on a per-query basis. To overcome this challenge, we propose to combine different state-of-the-art disjunctive top-k query processing algorithms to minimize the execution time by selecting the most promising one for each query. We model the selection step as a classification problem in a machine-learning setup. We conduct extensive experimentation and compare the results against state-of-the-art baselines using standard document collections and query sets. On ClueWeb12, our proposal shows a speed-up of up to 1.20x for non-blocked index organizations and 1.19x for block-based ones. Moreover, tail latencies are reduced showing proportional improvements on average, but a resulting dramatic decrease in latency variance. Given these findings, the proposed approach can be easily applied to existing search infrastructures to speed up query processing and reduce resource consumption, positively impacting providers’ operative costs.

A comparison of Apache Solr and Elasticsearch technologies in support of large-scale data analysis

In the era of big data, data has never been more important because it contains hidden insights. Additionally, it is necessary and challenging to extract usable information from enormous volumes of data. When attempting to perform data processing and analytics in a variety of domains, developers of data-intensive systems have consequently met several challenges. In addition, full-text search is one of the most significant components of big data processing and analytics for discovering fragments of required data among large volumes of data. Due to the importance of the subject, this article begins with an examination of the characteristics, capabilities, and technical comparisons of full-text search technologies, followed by a systematic comparison of Apache Solr and Elasticsearch in terms of indexing times and queries on three separate datasets. According to our findings, based on default configuration, Apache Solr has better performance when looking at indexing times measured on three machines with different hardware specifications. Likewise, Apache Solr outperforms Elasticsearch in seven out of ten search queries. Regarding our results, on computers with restricted hardware resources, we recommend utilizing Apache Solr instead of Elasticsearch. In addition, this study provides researchers and developers of data-intensive systems with a complete comparison and suggestions for choosing the most effective full-text search engine for their task.

Complex Queries for Querying Linked Data

Querying Linked Data is one of the most important issues for the semantic web community today because it requires the user to understand the structure and vocabularies used in various data sources. Furthermore, users must be familiar with the syntax of query languages, such as SPARQL. However, because users are accustomed to natural language-based search, novice users may find it challenging to use these features. As a result, new approaches for querying Linked Data sources on the web with NL queries must be defined. In this paper, we propose a novel system for converting natural language queries into SPARQL queries to query linked and heterogeneous semantic data on the web. While most existing methods have focused on simple queries and have ignored complex queries, the method described in this work aims to handle various types of NL queries, particularly complex queries containing negation, numbers, superlatives, and comparative adjectives. Three complementary strategies are used in this context: (1) identifying the semantic relations between query terms in order to understand the user’s needs; (2) mapping the NL terms to semantic entities; and (3) constructing the query’s valid triples based on the different links used to describe the identified entities in order to generate correct SPARQL queries. The empirical evaluations show that the proposed system is effective.

COVID-19 Related Early Google Search Behavior and Health Communication in the United States: Panel Data Analysis on Health Measures.

The COVID-19 outbreak at the end of December 2019 spread rapidly all around the world. The objective of this study is to investigate and understand the relationship between public health measures and the development of the pandemic through Google search behaviors in the United States. Our collected data includes Google search queries related to COVID-19 from 1 January to 4 April 2020. After using unit root tests (ADF test and PP test) to examine the stationary and a Hausman test to choose a random effect model, a panel data analysis is conducted to investigate the key query terms with the newly added cases. In addition, a full sample regression and two sub-sample regressions are proposed to explain: (1) The changes in COVID-19 cases number are partly related to search variables related to treatments and medical resources, such as ventilators, hospitals, and masks, which correlate positively with the number of new cases. In contrast, regarding public health measures, social distancing, lockdown, stay-at-home, and self-isolation measures were negatively associated with the number of new cases in the US. (2) In mild states, which ranked one to twenty by the average daily new cases from least to most in 50 states, the query terms about public health measures (quarantine, lockdown, and self-isolation) have a significant negative correlation with the number of new cases. However, only the query terms about lockdown and self-isolation are also negatively associated with the number of new cases in serious states (states ranking 31 to 50). Furthermore, public health measures taken by the government during the COVID-19 outbreak are closely related to the situation of controlling the pandemic.

Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature Recommendation

Tremendous academic articles face serious information overload problems while supporting literature searches. Finding a research article in a relevant domain that meets researchers’ requirements is challenging. Hence, different paper recommendation models have been proposed to address this issue. However, these models lack a more comprehensive analysis of the connections between the literature, the domain knowledge provided, and the hotspot information expressed in the literature. Previous models make it impossible to locate the appropriate documents for domain literature. Additionally, these models encounter problems such as cold start papers and data sparsity. To overcome these problems, this paper presents a recommendation model termed PRHN. Inputs of the model are the hotspot information network and the domain knowledge graph, which both were developed during the preceding research phase. After the query terms are extracted and the associated heterogeneous literature networks are formed, they are aggregated in a uniform hidden space. Similarity with the candidate set is determined to transform the search problem into a TOP N recommendation problem. Compared to state-of-the-art models, results generated by PRHN on public available datasets show improvement in HR and NDCG. Concretely, results on the metallurgical literature dataset are more conspicuous, with more remarkable improvement in HR and NGCC by approximately 4.5% and 4.2%.

Snippet-based result merging in federated search

In federated search, the central broker simultaneously forwards the search query to multiple resources. The returned results from those resources are then merged into a single ranked list. An autonomous resource in a federated search system usually does not provide scores for the retrieved documents; even if some of them do, scores from different resources are incomparable due to the heterogeneity in many aspects of those resource involved such as retrieval models and corpus statistics. Many results merging approaches have been proposed in the literature to deal with this problem. However, to the best of our knowledge, none of them has utilised snippets. This article proposes a snippet-based weighting scheme for the query terms involved. It quantifies the importance of each query term from two angles: the frequency of the term and the part in which the term occurs inside a snippet. Three parts – which are URL, title, and description – are given different weights. Experiments are conducted with the TREC 2013 FedWeb data set. The results show that the proposed methods consistently outperform several baseline models. We also find in many instances, a further small slight performance improvement is achievable by an extra measure of weighting each of the resources involved, which can be done in the phase of resource selection.

The disparities in clinical trials addressing urologic conditions among lower-income countries

IntroductionGenitourinary pathologies are increasingly common in low and lower-middle Q6 income countries (LMICs) however there is a disproportionate distribution of clinical trials in higher income countries as compared to LMICs. In order for evidence-based practice to be implemented in LMICs with regards to urologic conditions and malignancies, clinical trials need to be performed within LMICs such that the results can be analyzed based on the context of the local environment.MethodsWe queried ClinicalTrials.gov and the ICTRP for active clinical trials that were related to ‘prostatic neoplasms’, ‘urinary bladder neoplasms’, ‘kidney neoplasms’, ‘urolithiasis’, ‘urinary tract infections’ and ‘lower urinary tract symptoms’. The national incidence and DALYs was obtained from the Global Burden of Disease 2019 to analyze for a correlation between the numbers of clinical trials performed in a country with the burden of disease.ResultsA total of 4,169 clinical trials were identified based on the search query terms. Ninety percent of the clinical trials are being conducted in 32 countries. A majority of clinical trials are being performed in HICs. The proportion of non-oncologic urologic clinical trials performed in LMICs is greater than the proportion of urologic oncology clinical trials performed in LMICs (p <0.001). Linear regression models demonstrates a weak relationship between the global burden of disease and the number of clinical trials conducted in each country for the individual urologic conditions.DiscussionA majority of urologic clinical trials are being conducted in high-income countries which does not coincide with the global burden of disease of urologic conditions.

A Relative Information Gain-based Query Performance Prediction Framework with Generated Query Variants

Query performance prediction (QPP) methods, which aim to predict the performance of a query, often rely on evidences in the form of different characteristic patterns in the distribution of Retrieval Status Values (RSVs). However, for neural IR models, it is usually observed that the RSVs are often less reliable for QPP because they are bounded within short intervals, different from the situation for statistical models. To address this limitation, we propose a model-agnostic QPP framework that gathers additional evidences by leveraging information from the characteristic patterns of RSV distributions computed over a set of automatically generated query variants, relative to that of the current query. Specifically, the idea behind our proposed method—Weighted Relative Information Gain (WRIG), is that a substantial relative decrease or increase in the standard deviation of the RSVs of the query variants is likely to be a relative indicator of how easy or difficult the original query is. To cater for the absence of human-annotated query variants in real-world scenarios, we further propose an automatic query variant generation method. This can produce variants in a controlled manner by substituting terms from the original query with new ones sampled from a weighted distribution, constructed either via a relevance model or with the help of an embedded representation of query terms. Our experiments on the TREC-Robust, ClueWeb09B, and MS MARCO datasets show that WRIG, by the use of this relative changes in QPP estimate, leads to significantly better results than a state-of-the-art baseline method that leverages information from (manually created) query variants by the application of additive smoothing [ 64 ]. The results also show that our approach can improve the QPP effectiveness of neural retrieval approaches in particular.

The effects of long COVID-19, its severity, and the need for immediate attention: Analysis of clinical trials and Twitter data.

The coronavirus disease 2019 (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization; identifying the disease progression, predicting patient outcomes early, the possibility of long-term adverse events through effective modeling, and the use of real-world data are of immense importance to effective treatment, resource allocation, and prevention of severe adverse events of grade 4 or 5. First, we raise awareness about the different clinical trials on long COVID-19. The trials were selected with the search term "long COVID-19" available in ClinicalTrials.gov. Second, we curated the recent tweets on long-haul COVID-19 and gave an overview of the sentiments of the people. The tweets obtained with the query term #long COVID-19 consisted of 8,436 tweets between 28 August 2022 and 06 September 2022. We utilized the National Research Council (NRC) Emotion Lexicon method for sentiment analysis. Finally, we analyze the retweet and favorite counts are associated with the sentiments of the tweeters via a negative binomial regression model. Our results find that there are two types of clinical trials being conducted: observational and interventional. The retweet counts and favorite counts are associated with the sentiments and emotions, such as disgust, joy, sadness, surprise, trust, negative, and positive. We need resources and further research in the area of long COVID-19.

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

SummaryDue to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy‐based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean‐Average Precision, F‐measure, Precision‐recall, and Precision rank are evaluated and analyzed by using TREC‐COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state‐of‐art works. For a total of 60 queries, the proposed model offers an F1‐Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1‐score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

The Status of Insulin Access in Middle East-North Africa Region

Background and Objective: The current situation in the Middle East-North Africa (MENA) region makes it very difficult for many countries to have an effective policy to ensure the availability and affordability of different insulin types for many individuals with diabetes mellitus in any individual country. This article comprehensively reviews the possible barriers to insulin access in countries of this region and provides some solutions to mitigate these barriers. Materials and Methods: The framework for understanding the life-cycle of medicines of the World Health Organization (WHO) was adopted for this review with country-specific modifications. PubMed was used as an initial search builder using the country name and insulin as query terms in (title/abstract) for articles written in (English and French) which dealt with humans only between (2000–2022). Out of (578) published articles, only (42) articles were relevant and dealt with insulin access. Additional search through references of these articles and the websites of international health organizations added additional (32) references to have (79) references for the review. Review (Results): The countries in the MENA are lagging in the (Research, Development, and Innovation) and production of insulin and insulin delivery systems. There are some attempts by some countries to develop their national plan with the help of some big insulin manufacturers, with a suboptimal outcome. The local insulin production in some countries did not reach full national coverage. The difficult regional economic and political situations in many countries in the MENA imposed negatively on their citizens’ insulin access, availability, affordability, cost, and insurance plans. Conclusions: There is a suboptimal level of insulin access for individuals with diabetes in the MENA countries, especially in the low-resourced countries. There is an urgent need to adopt country-specific healthcare policies to ensure uninterrupted insulin access and improved availability, with a lower affordable cost for individuals with diabetes from the public and private sectors.

Using clarification questions to improve software developers’ Web search

Context: Recent research indicates that Web queries written by software developers are not very successful in retrieving relevant results, performing measurably worse compared to general purpose Web queries. Most approaches up to this point have addressed this problem with software engineering-specific automated query reformulation techniques, which work without developer involvement but are limited by the content of the original query. In other words, these techniques automatically improve the existing query but can not contribute new, previously unmentioned, concepts. Objective: In this paper, we propose a technique to guide software developers in manually improving their own Web search queries. We examine a conversational approach that follows unsuccessful queries with a clarification question aimed at eliciting additional query terms, thus providing to the developer a clear dimension along which the query could be improved. Methods: We describe a set of clarification questions derived from a corpus of software developer queries and a neural approach to recommending them for a newly issued query. Results: Our evaluation indicates that the recommendation technique is accurate, predicting a valid clarification question 80% of the time and outperforms simple baselines, as well as, state-of-the-art Learning To Rank (LTR) baselines. Conclusion: As shown in the experimental results, the described approach is capable at recommending appropriate clarification questions to software developers and considered useful by a sample of developers ranging from novices to experienced professionals.

Scientific Research Directions on the Histopathology and Immunohistochemistry of the Cutaneous Squamous Cell Carcinoma: A Scientometric Study.

Introduction: Cutaneous squamous cell carcinoma (cSCC) is one of the most frequently occurring types of cancer in humans. Scientometric research is an innovative method for analyzing the research trends in various domains, with great implications in the field of medicine. Materials and Methods: We searched the Web of Science database with the following established query terms: “Squamous cell carcinoma”, “skin”, and “immunohistochemistry”. After applying the inclusion and exclusion criteria, a total of 76 articles were selected. The present study aims to analyze, based on the frequency of use of keywords with scientometric algorithms and map-based distributions, the trends of the research concerning cSCCs in 2017–2022. Results: A graphical representation based on 11 scientometric maps presented the division of the keywords into seven clusters, from which seven categories of research interest were defined. The clusters represent a multidisciplinary approach to the diagnosis and treatment of cSCCs, cancer diagnostics, patient outcomes, histopathological importance, management of cSCCs, role of progression, and adequate treatment of and importance of immunohistochemistry for cSCCs. The distribution of the citations shows the importance of the available research on cSCCs by analyzing the first five most-cited articles included in our study in direct concordance with the seven defined clusters. Conclusion: The scientometric research method reveals the interest of research in the multidisciplinary approach used to obtain the best outcomes for the patient, including a targeted investigation, as well as diagnostic and treatment options. The trends in the research reveal that histopathological diagnostics and immunohistochemistry, combined with molecular techniques, are the most important tools used to establish a personalized diagnosis, thus increasing the quality of life and life expectancy for patients with cSCCs.

Enhanced strategy for adaptive Cartesian grid generation with arbitrarily complex 3D geometry

This article presents an enhanced strategy for generating Cartesian grids for arbitrary complex three-dimensional (3D) geometry. It addresses the major challenges, including data structures managing surface triangles and Cartesian cells, background Cartesian generation, intersection cell determination, and adaptive mesh refinement. To overcome the problem of inefficiency, we developed a robust method for determining intersecting cells based on the k-dimensional tree data structure combined with the fast 3D-Cartesian box-triangle overlap testing algorithm. Furthermore, the minimum distance query algorithm based on the k-dimensional tree was enhanced to improve the query efficiency by reducing the number of backtrackings. The correctness and efficiency of the proposed strategy were evaluated and verified by 3D tests on different configurations. The results show that in terms of mesh generation, the CPUtime∕cell was nearly 5.33–5.54×10−6 s; in terms of the minimum distance query, the query efficiency of the improved backtracking method improved by up to 870.9 times compared with the traversal method. Meshing experiments of several complicated configurations demonstrated that the proposed algorithms were accurate, robust, and efficient. At last, an example was examined, and the result demonstrates that the mesh generation strategy developed in this study meets the actual flow field calculation requirements.

Periodic Trends in Internet Searches for Ocular Symptoms in the US

ABSTRACT Purpose To identify periodic trends in internet searches for ocular symptoms and to determine the seasonal peaks and troughs. Methods This cross-sectional study examined publicly available Google Trends data from the United States (01/01/2015 to 12/31/2019). A list of common ocular symptoms was compiled from the American Academy of Ophthalmology Eye Health website and Wills Eye Manual. Ocular symptoms were stratified into categories involving vision change, eye pain, or eye redness. The search volume over time for each term was modeled using periodic regression functions and the goodness-of-fit was reported. Fisher’s exact tests were used to compare the characteristics of periodic vs. non-periodic query terms. Results Seasonal trends were demonstrated by 45% (48/106) of the ocular symptoms included in this investigation. Search terms with best fit to the periodic model included stye (r2 = 0.89), pink eye (r2 = 0.82), dry eye (r2 = 0.76), blurry vision (r2 = 0.72), and swollen eye (r2 = 0.71). Periodic search terms were more likely to involve eye redness (21% vs. 11%, p = .014) and less likely to involve vision change (11% vs. 36%; p < .001). Periodic queries involving eye redness most often peaked in the spring and those involving eye pain peaked in the summer. Conclusion Ocular symptom queries directly reflect seasonal trends for allergic eye disease and ocular trauma. Search query analyses can serve as accurate epidemiological tools with research and real-world clinical applications.

Improving ascertainment of suicidal ideation and suicide attempt with natural language processing

Methods relying on diagnostic codes to identify suicidal ideation and suicide attempt in Electronic Health Records (EHRs) at scale are suboptimal because suicide-related outcomes are heavily under-coded. We propose to improve the ascertainment of suicidal outcomes using natural language processing (NLP). We developed information retrieval methodologies to search over 200 million notes from the Vanderbilt EHR. Suicide query terms were extracted using word2vec. A weakly supervised approach was designed to label cases of suicidal outcomes. The NLP validation of the top 200 retrieved patients showed high performance for suicidal ideation (area under the receiver operator curve [AUROC]: 98.6, 95% confidence interval [CI] 97.1–99.5) and suicide attempt (AUROC: 97.3, 95% CI 95.2–98.7). Case extraction produced the best performance when combining NLP and diagnostic codes and when accounting for negated suicide expressions in notes. Overall, we demonstrated that scalable and accurate NLP methods can be developed to identify suicidal behavior in EHRs to enhance prevention efforts, predictive models, and precision medicine.

Seasonal Variation for Plantar Fasciitis: Evidence from Google Trends Search Query Data.

We aimed to determine the seasonal trends in internet searches for plantar fasciitis and related symptoms in various countries using search engine query data on Google. We used Google Trends to obtain internet search query data from January 2009 to December 2019. We collected monthly search volumes for the query terms “plantar fasciitis” and “heel pain” in the USA, Canada, the U.K., Ireland, Australia, and New Zealand. Statistical analysis of the seasonal effects on plantar fasciitis was performed using a cosinor model. The cosinor analyses confirmed statistically significant seasonal patterns in the relative search volumes for the terms “plantar fasciitis” and “heel pain” in the USA, Canada, the U.K., Ireland, and Australia, with peaks during the summer and troughs during the winter. For New Zealand, the seasonal trend was statistically significant only for the term “plantar fasciitis”, while a similar trend for the term “heel pain” was present without achieving statistical significance for seasonality. This seasonality is thought to be related to more frequent occurrence of plantar fasciitis due to increased physical activity of people during the warmer months. In this study, the search query data using the terms “plantar fasciitis” and “heel pain” on Google Trends show significant seasonal variation across several countries, with a peak in the summer and a trough in the winter.

Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval.

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques.

Reverse spatial top-k keyword queries

We introduce the ReverseSpatial Top-kKeyword (RSK) query, which is defined as: given a query term q, an integer k and a neighborhood size find all the neighborhoods of that size where q is in the top-k most frequent terms among the social posts in those neighborhoods. An obvious approach would be to partition the dataset with a uniform grid structure of a given cell size and identify the cells where this term is in the top-k most frequent keywords. However, this answer would be incomplete since it only checks for neighborhoods that are perfectly aligned with the grid. Furthermore, for every neighborhood (square) that is an answer, we can define infinitely more result neighborhoods by minimally shifting the square without including more posts in it. To address that, we need to identify contiguous regions where any point in the region can be the center of a neighborhood that satisfies the query. We propose an algorithm to efficiently answer an RSK query using an index structure consisting of a uniform grid augmented by materialized lists of term frequencies. We apply various optimizations that drastically improve query latency against baseline approaches. We also provide a theoretical model to choose the optimal cell size for the index to minimize query latency. We further examine a restricted version of the problem (RSKR) that limits the scope of the answer and propose efficient approximate algorithms. Finally, we examine how parallelism can improve performance by balancing the workload using a smart load slicing technique. Extensive experimental performance evaluation of the proposed methods using real Twitter datasets and crime report datasets, shows the efficiency of our optimizations and the accuracy of the proposed theoretical model.