Query Interval Research Articles

Searching rooms with top-k passenger flows using indoor trajectories

In a wide variety of applications, such as indoor position selection for advertising and setting rents of different shops in a shopping mall, it is better to get the passenger flow of each room. In the indoor space, the positions of users are commonly captured by the indoor positioning system consisting of static positioning devices. And the sequence of all tracking events with the same user ordered by the corresponding time is the indoor trajectory of this user. Thus, in this paper, we define and study two essential queries named Rooms with top-k passenger flows at a Timestamp query (RkT for short) and Rooms with top-k passenger flows within a time Interval query (RkI for short), i.e., how to search rooms with top-k passenger flows at a timestamp and within a time interval in the past using indoor trajectories, respectively. For the indoor positioning system, there are only limited static positioning devices deployed in the indoor space on account of the cost. And the detection ranges of these static positioning devices only cover a small part of the indoor space. When a user is in the undetected state, there is uncertainty in its position combined with the quite complex indoor topology. Such uncertainty brings great challenges to determining the passenger flow in each room. Considering the distribution of static positioning devices, we propose a new method about how to reasonably infer where a user is in the undetected state and the corresponding probability based on its indoor trajectory and the complex indoor topology. In order to quickly retrieve the set of indoor trajectories, we propose a full Binary tree indexing indoor trajectories divided by Time intervals (BiT for short), which is built on the given set of indoor trajectories. Based on the index BiT, we propose PAT Algorithm and PAI Algorithm to efficiently process RkT and RkI queries, respectively. Extensive experiment results demonstrate superior performance of PAT Algorithm and PAI Algorithm.

TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning

Conventional embedding-based models approach event time prediction in temporal knowledge graphs (TKGs) as a ranking problem. However, they often fall short in capturing essential temporal relationships such as order and distance. In this paper, we propose TEILP, a logical reasoning framework that naturaly integrates such temporal elements into knowledge graph predictions. We first convert TKGs into a temporal event knowledge graph (TEKG) which has a more explicit representation of time in term of nodes of the graph. The TEKG equips us to develop a differentiable random walk approach to time prediction. Finally, we introduce conditional probability density functions, associated with the logical rules involving the query interval, using which we arrive at the time prediction. We compare TEILP with state-of-the-art methods on five benchmark datasets. We show that our model achieves a significant improvement over baselines while providing interpretable explanations. In particular, we consider several scenarios where training samples are limited, event types are imbalanced, and forecasting the time of future events based on only past events is desired. In all these cases, TEILP outperforms state-of-the-art methods in terms of robustness.

Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering

In relational and object-oriented database systems there is always data that is naturally fuzzy or uncertain. However, to deal with complex data types with fuzzy nature, these systems have many limitations. Therefore, in order to represent and manage fuzzy data, it is necessary to have a fuzzy interrogation system to facilitate non-expert users. To solve this challenge, the paper proposes two different approaches to increase the flexibility of the fuzzy interrogation system. Firstly, based on similarity measures and fuzzy logic, we develop three fuzzy query processing algorithms for single-condition and multi-condition cases such as FQSIMSC (Fuzzy Query Sim Single Condition), FQSIMMC (Fuzzy Query Sim Multi-Condition) and FQSEM (Fuzzy Query SEM). Secondly, we combine the fuzzy clustering algorithm EMC (Expectation maximization Coefficient) and the query processing algorithm that is based on fuzzy partitions FQINTERVAL (Fuzzy Query Interval). With this approach, we not only improve query processing cost but also support applications and devices equipped with intelligent interactive function that easily interacts with the fuzzy query system. The results of our theoretical and experimental analysis, it can be seen that both the proposed methods significantly reduce the processing time and memory space for a data set (extracted from UCI) that has a fuzzy and incomplete natural element with the resulting data size being optimal

Hypertension and Related-Disorders in Hodgkin Lymphoma Patients Identified from Epic Cosmos System

Recent advancements in Hodgkin lymphoma (HL) treatment have significantly improved disease-free survival rates. However, survivors face a higher burden of long-term comorbidities and non-oncologic mortality compared to their peers. Studying survivorship has been challenging due to the time required for comorbidities to develop, patient loss to follow-up, variable disease-specific treatments, and variations across age groups of survivors. In this context, the Cosmos system offers a validated population-based informatics approach to establishing observational relationships between therapy and longitudinal non-oncologic outcomes, with the potential to enhance clinical care. Cosmos, a data science tool based on the Epic Electronic Medical Record (EMR) system, utilizes a HIPAA-defined limited centralized data set from various US health care systems. At the time of this submission, the data set encompasses 152,450,619 patients. A query population of 89,963 HL patients with more than one face-to-face encounter within any two-year period during the query interval was established. Validated associated ICD-10 diagnoses were assessed within the Cosmos system. The query revealed that HL patients had a median age of 56, with a male predominance (51.8%), and 35% were diagnosed between the ages of 15 and 39. Staging information was available for only 10% of patients. The most common comorbid diagnoses defined by ICD-10 were essential hypertension (45.4%), hyperlipidemia (36.1%), anemia (32.1%), GERD (32.1%), shortness of breath (31.4%), fatigue (30.1%), non-HL (30%), cough (26.3%), and anxiety (25.6%). Further assessment of hypertension and associated disorders was conducted by querying Epic grouper terms in comparison to the general Cosmos population. The HL population had a higher prevalence of hypertension (46.1% vs. 24.5%), chronic kidney disease (14.9% vs. 5%), hypertensive renal disease (10.1% vs. 3.1%), hyperlipidemia (44% vs. 21.1%), coronary artery disease (20.7% vs. 6.2%), ischemic or unspecified stroke (4.4% vs. 1.6%), and myocardial infarction (9.6% vs. 2.8%). Notably, death dates were documented for 12.5% of the HL population (compared to 3% in the general Cosmos population), but cause of death information was limited (0.028%). EMR informatics presents a powerful tool for rapidly generating real-time observational data, which can inform focused studies and interventions. Such data is crucial for understanding trends in treatment-related morbidity and mortality in diseases like HL, which can impact future health in survivorship. This study suggests that hypertension and related disorders may be more prevalent in the HL population compared to the general Cosmos population, warranting further directed study and validation in the future.

Online evaluation of the Kolmogorov–Smirnov test on arbitrarily large samples

This paper presents an approximative online algorithm to perform the Kolmogorov–Smirnov test. There is a ubiquitous need for evaluating the fitness between statistical distributions and data samples, which this test conveniently meets. Taking some inspiration from the challenges of detecting concept drifts in data streams, our methodology shows how this goodness-of-fit statistical test can be used to detect such events, taking advantage of the fact that it is non-parametric and could be adapted to handle streams while keeping its original relatively small algorithmic complexity. The presented work focused on the one-sample test, which evaluates the hypothesis that a given univariate sample follows some reference distribution, for assessing an input stream with high precision in a time- and space-efficient fashion. The performance of our algorithm and some of the state-of-the-art methods were compared using synthetic and real data. We evaluated the accuracy, effectiveness and efficiency of these methods by making extensive experiments in multiple scenarios: varying reference distribution and its parameters, sample size, available memory, drift point and query interval. The results showed that our algorithm is advantageous in most cases, even with substantial restrictions of computational resources.

E-Learning Virtual Platform with Attendance and Query Using Deep Learning

Abstract: The COVID-19 epidemic outbreak has rebounded in an unknown extrimity across the globe. The epidemic created an enormousdemand for innovative technologies to break extremity-specific problems in different sectors of society. Inthe case of the education sector and confederated literacy technologies, significant issues have surfaced while substituting face-to-face literacy with onlinevirtual literacy. Several countries have closed educational institutions temporarily to palliate theCOVID-19 spread. The check of educational institutions impelled the preceptors across the globe to use online meeting platforms considerably. The virtual classrooms created by online meeting platforms are espoused as the only volition for face-to face commerce in physical classrooms. In this regard, scholars’ attendance operation in virtual classes is a major challenge encountered bythe preceptors. Pupil attendance is a measure of their engagement in a course, which has a direct relationship with their active literacy. Still, during virtual literacy, it is exceptionally grueling to keep track of the attendance ofscholars. Calling scholars names in virtual classroom to take attendance is both trivial and time-consuming. Therefore, in the background of the COVID-19 epidemic and the operation of virtual meeting platforms, there is a extremity-specific immediate necessity to develop a proper shadowing system to cover scholars attendance and engagement during virtual literacy. In this design, we are addressing the epidemic -convinced pivotal necessity by introducing a new approach.In order to realize a largely effective and robust attendance operation system for virtual literacy, we introduce the Random Interval Query and Face Recognition Attendance ManagementSystem(henceforth, AI Present).To the stylish of our knowledge no similar automated system has been proposed so far for tracking scholars attendance and icing their engagement during virtual literacy

Staggered HLL: Near-continuous-time cardinality estimation with no overhead

Most of existing cardinality estimation algorithms do not support natively interval queries under a sliding window model and are thereby insensitive to data recency. We present Staggered-HyperLogLog (ST-HLL), a probabilistic data structure that takes inspiration from HyperLogLog (HLL) and provides nearly continuous-time estimation of cardinality rates, rather than absolute counts. Our solution has zero-bit overhead with respect to vanilla HLL and negligible additional computational complexity. It is based on a periodic staggered reset of HLL registers and a register equalization operation at query times to compensate for staggered counting. We tested ST-HLL over both synthetic and real Internet traffic traces, showing its ability to track variations of the flow cardinality, quickly adapting to variations under non-stationary flow arrival processes. We show that for the same amount of memory footprint, our algorithm improves the accuracy up to a factor 2x with respect to the state-of-the-art solution, Sliding HLL.

A Distributed Hybrid Indexing for Continuous KNN Query Processing over Moving Objects

The magnitude of highly dynamic spatial data is expanding rapidly due to the instantaneous evolution of mobile technology, resulting in challenges for continuous queries. We propose a novel indexing approach model, namely, the Velocity SpatioTemporal indexing approach (VeST), for continuous queries, mainly Continuous K-nearest Neighbor (CKNN) and continuous range queries using Apache Spark. The proposed structure is based on a selective velocity partitioning method, i.e., since different objects have varying speeds, we divide the objects into two sets according to the actual mean speed we calculate before building the index and accessing data. Then the adopted indexing structure base unit comprises a nonoverlapping R-tree and a two dimension grid. The tree divides the space into nonoverlapping minimum bounding regions that point to the grids. Then, the uniform grid stores the object data of leaf nodes. This access method reduces the update cost and improves response time and query precision. In order to enhance performances for large-scale processing, we design a compact multilayer index structure on a distributed setting and propose a CKNN search algorithm for accurate results using a candidate cell identification process. We provide a comprehensive vision of our indexing model and the adopted query technique. The simulation results show that for query intervals of 100, the proposed approach is 13.59 times faster than the traditional approach, and the average time of the VeST approach is less than 0.005 for all query intervals. This proposed method improves response time and query precision. The precision of the VeST algorithm is almost equal to 100% regardless of the length of the query interval.

An efficient and scalable method for aggregate nearest neighbor queries on time-dependent road networks

We study the k aggregate nearest neighbor queries on time-dependent road networks (TDRNs), assuming that the travel time to traverse an edge depends on the time it is initiated. Given a set of query points, a set of points of interest (POIs), an aggregate function (e.g., max) and a number k, the query returns k POIs p in increasing order of the values of the aggregate travel times from the query points to p. We investigate two forms of queries. The single departure time query requires all the query points to simultaneously start at a fixed departure time given as a query condition. In addition, we propose a more flexible form of query, the time interval query that allows the query points to independently depart at any arbitrary time in a given departure time interval.Existing methods are either too inefficient or sensitive to query factors, i.e., the query running time is greatly affected by some factors like the number or the distributions of query points and POIs. We propose an efficient and scalable method to answer the two forms of queries using time-dependent G-Tree (TDGtree). TDGtree is a tree-structured index that could efficiently return the minimum travel time between two locations in a TDRN. We devise a function to evaluate which regions are likely to contain the promising POIs, and we employ a minimum-first-search strategy to preferentially visit the promising regions so that we could efficiently pinpoint the k optimal POIs. Experiments by varying a number of factors demonstrate the superiority of our method. In terms of query efficiency, our method achieves query speed faster than the states-of-the-arts up to two orders of magnitude. In the aspect of scalability, our method has relatively steady query running time regardless of the query parameter value changes.

Managing Big Interval Data with CINTIA: The Checkpoint INTerval Array

Intervals have become prominent in data management as they are the main data structure to represent a number of key data types such as temporal or genomic data. Yet, there exists no solution to compactly store and efficiently query big interval data. In this paper we introduce CINTIA-the Checkpoint INTerval Index Array-an efficient data structure to store and query interval data, which achieves high memory locality and outperforms state-of-the art solutions. We also propose a low-latency, Big Data system that implements CINTIA on top of a popular distributed file system and efficiently manages large interval data on clusters of commodity machines. Our system can easily be scaled-out and was designed to accommodate large delays between the various components of a distributed infrastructure. We experimentally evaluate the performance of our approach on several datasets and show that it outperforms current solutions by several orders of magnitude in distributed settings.

A Node Virtualization Scheme for Structured Overlay Networks Based on Multiple Different Time Intervals

Sensor data which relate to the specific geographical positions, areas, and time are strongly expected in IoT. The author has studied overlay networks to efficiently process interval queries which have specific time intervals and the actual users tend to request. However, unfairness and a concentration of the loads occur for the specific processing computer (node) in the previous method because the density of data or those generators/providers is different from those related values. In this paper, the author proposes the enhanced scheme for structured overlay networks based on multiple different time intervals. The proposed method uses node virtualization to equalize the loads of each real (physical) node. The simulation results showed that the proposed method can increase the fairness of the number of the assigned data among physical nodes.

Space-efficient algorithms for computing minimal/shortest unique substrings

Given a string T of length n, a substring u=T[i..j] of T is called a shortest unique substring (SUS) for an interval [s,t] if (a) u occurs exactly once in T, (b) u contains the interval [s,t] (i.e. i≤s≤t≤j), and (c) every substring v of T with |v|<|u| containing [s,t] occurs at least twice in T. Given a query interval [s,t]⊂[1,n], the interval SUS problem is to output all the SUSs for the interval [s,t]. In this article, we propose a 4n+o(n) bits data structure answering an interval SUS query in output-sensitive O(occ) time, where occ is the number of returned SUSs. Additionally, we focus on the point SUS problem, which is the interval SUS problem for s=t. Here, we propose a ⌈(log2⁡3+1)n⌉+o(n) bits data structure answering a point SUS query in the same output-sensitive time. We also propose space-efficient algorithms for computing the minimal unique substrings of T.

Privacy Preserving Visual Log Service with Temporal Interval Query using Interval Tree-based Searchable Symmetric Encryption

Visual logs become widely available via personal cameras, visual sensors in smart environments, or surveillance systems. Storing such data in public services is a common convenient solution, but it is essential to devise a mechanism to encrypt such data to protect sensitive information while enabling the capability to query visual content even in encrypted format at the services. More precisely, we need smart systems that their security and practicality must be balanced against each other. As far as we know, in spite of their importance in preserving personal privacy, such reliable systems have not gained sufficient attention from researchers. This motivates our proposal to develop a smart secure service for visual logs with a temporal interval query. In our system, visual log data are analyzed to generate high-level contents, including entities, scenes, and activities happening in visual data. Then our system supports data owners to query these high-level contents from their visual logs at the server-side in a temporal interval while the data are still encrypted. Our searchable symmetric encryption scheme TIQSSE utilizes interval tree structure and we prove that our scheme achieves efficient search and update time while also maintaining all important security properties such as forward privacy, backward privacy, and it does not leak information outside the desired temporal range.

Fast Algorithms for the Shortest Unique Palindromic Substring Problem on Run-Length Encoded Strings

For a string $S$, a palindromic substring $S[i..j]$ is said to be a \emph{shortest unique palindromic substring} ($\mathit{SUPS}$) for an interval $[s, t]$ in $S$, if $S[i..j]$ occurs exactly once in $S$, the interval $[i, j]$ contains $[s, t]$, and every palindromic substring containing $[s, t]$ which is shorter than $S[i..j]$ occurs at least twice in $S$. In this paper, we study the problem of answering $\mathit{SUPS}$ queries on run-length encoded strings. We show how to preprocess a given run-length encoded string $\mathit{RLE}_{S}$ of size $m$ in $O(m)$ space and $O(m \log \sigma_{\mathit{RLE}_{S}} + m \sqrt{\log m / \log\log m})$ time so that all $\mathit{SUPSs}$ for any subsequent query interval can be answered in $O(\sqrt{\log m / \log\log m} + \alpha)$ time, where $\alpha$ is the number of outputs, and $\sigma_{\mathit{RLE}_{S}}$ is the number of distinct runs of $\mathit{RLE}_{S}$. Additionaly, we consider a variant of the SUPS problem where a query interval is also given in a run-length encoded form. For this variant of the problem, we present two alternative algorithms with faster queries. The first one answers queries in $O(\sqrt{\log\log m /\log\log\log m} + \alpha)$ time and can be built in $O(m \log \sigma_{\mathit{RLE}_{S}} + m \sqrt{\log m / \log\log m})$ time, and the second one answers queries in $O(\log \log m + \alpha)$ time and can be built in $O(m \log \sigma_{\mathit{RLE}_{S}})$ time. Both of these data structures require $O(m)$ space.

The Most Likely Object to be Seen Through a Window

We study data structures to answer window queries using stochastic input sequences. The first problem is the most likely maximal point in a query window: Let [Formula: see text] be constants, with [Formula: see text]. Let [Formula: see text] be a set of [Formula: see text] points in [Formula: see text], for some fixed [Formula: see text]. For [Formula: see text], each point in [Formula: see text] is associated with a probability [Formula: see text] of existence. A point [Formula: see text] in [Formula: see text] is on the maximal layer of [Formula: see text] if there is no other point [Formula: see text] in [Formula: see text] such that [Formula: see text]. Consider a random subset of [Formula: see text] obtained by including, for [Formula: see text], each point of [Formula: see text] independently with probability [Formula: see text]. For a query interval [Formula: see text], with [Formula: see text], we report the point in [Formula: see text] that has the highest probability to be on the maximal layer of [Formula: see text] in [Formula: see text] time using [Formula: see text] space. We solve a special problem as follows. A sequence [Formula: see text] of [Formula: see text] points in [Formula: see text] is given ([Formula: see text]), where each point [Formula: see text] has a probability [Formula: see text] of existence associated with it. Given a query interval [Formula: see text] and an integer [Formula: see text] with [Formula: see text], we report the probability of [Formula: see text] to be on the maximal layer of [Formula: see text] in [Formula: see text] time using [Formula: see text] space. The second problem we consider is the most likely common element problem. Let [Formula: see text] be the universe. Let [Formula: see text] be a sequence of random subsets of [Formula: see text] such that for [Formula: see text] and [Formula: see text], element [Formula: see text] is added to [Formula: see text] with probability [Formula: see text] (independently of other choices). Let [Formula: see text] be a fixed real number with [Formula: see text]. For query indices [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], with [Formula: see text] and [Formula: see text], we decide whether there exists an element [Formula: see text] with [Formula: see text] such that [Formula: see text] in [Formula: see text] time using [Formula: see text] space and report these elements in [Formula: see text] time, where [Formula: see text] is the size of the output.

Augmented Interval List: a novel data structure for efficient genomic interval search.

Genomic data is frequently stored as segments or intervals. Because this data type is so common, interval-based comparisons are fundamental to genomic analysis. As the volume of available genomic data grows, developing efficient and scalable methods for searching interval data is necessary. We present a new data structure, the Augmented Interval List (AIList), to enumerate intersections between a query interval q and an interval set R. An AIList is constructed by first sorting R as a list by the interval start coordinate, then decomposing it into a few approximately flattened components (sublists), and then augmenting each sublist with the running maximum interval end. The query time for AIList is O(log2N+n+m), where n is the number of overlaps between R and q, N is the number of intervals in the set R and m is the average number of extra comparisons required to find the n overlaps. Tested on real genomic interval datasets, AIList code runs 5-18 times faster than standard high-performance code based on augmented interval-trees, nested containment lists or R-trees (BEDTools). For large datasets, the memory-usage for AIList is 4-60% of other methods. The AIList data structure, therefore, provides a significantly improved fundamental operation for highly scalable genomic data analysis. An implementation of the AIList data structure with both construction and search algorithms is available at http://ailist.databio.org. Supplementary data are available at Bioinformatics online.

Efficient Genomic Interval Queries Using Augmented Range Trees

Efficient large-scale annotation of genomic intervals is essential for personal genome interpretation in the realm of precision medicine. There are 13 possible relations between two intervals according to Allen’s interval algebra. Conventional interval trees are routinely used to identify the genomic intervals satisfying a coarse relation with a query interval, but cannot support efficient query for more refined relations such as all Allen’s relations. We design and implement a novel approach to address this unmet need. Through rewriting Allen’s interval relations, we transform an interval query to a range query, then adapt and utilize the range trees for querying. We implement two types of range trees: a basic 2-dimensional range tree (2D-RT) and an augmented range tree with fractional cascading (RTFC) and compare them with the conventional interval tree (IT). Theoretical analysis shows that RTFC can achieve the best time complexity for interval queries regarding all Allen’s relations among the three trees. We also perform comparative experiments on the efficiency of RTFC, 2D-RT and IT in querying noncoding element annotations in a large collection of personal genomes. Our experimental results show that 2D-RT is more efficient than IT for interval queries regarding most of Allen’s relations, RTFC is even more efficient than 2D-RT. The results demonstrate that RTFC is an efficient data structure for querying large-scale datasets regarding Allen’s relations between genomic intervals, such as those required by interpreting genome-wide variation in large populations.

Near-Optimal Data Structure for Approximate Range Emptiness Problem in Information-Centric Internet of Things

The approximate range emptiness problem requires a memory-efficient data structure D to approximately represent a set S of n distinct elements chosen from a large universe U= {0,1,⋯,N-1} and answer an emptiness query of the form “S∩[a;b]=0?” for an interval [a;b] of length L (a,b∈U), with a false positive rate e. The designed D for this problem can be kept in high-speed memory and quickly determine approximately whether a query interval is empty or not. Thus, it is crucial for facilitating online query processing in the information-centric Internet of Things applications, where the IoT data are continuously generated from a large number of resource-constrained sensors or readers and then are processed in networks. However, the existing works on the approximate range emptiness problem only consider the simple case when the set S is static, rendering them unsuitable for the continuously generated IoT data. In this paper, we study the approximate range emptiness problem over sliding windows in the IoT Data streams, denoted by e-ARESD-problem, where both insertion and deletion are allowed. We first prove that, given a sliding window size n and an interval length L, the lower bound of memory bits needed in any data structure for e-ARESD-problem is n log 2 (nL/e)+Θ(n). Then, a data structure is proposed and proved to be within a factor of 1.33 of the lower bound. The extensive simulation results demonstrate the advantage of the efficiency of our data structure over the baseline approach.

Stream frequency over interval queries

Stream frequency measurements are fundamental in many data stream applications such as financial data trackers, intrusion-detection systems, and network monitoring. Typically, recent data items are more relevant than old ones, a notion we can capture through a sliding window abstraction. This paper considers a generalized sliding window model that supports stream frequency queries over an interval given at query time. This enables drill-down queries, in which we can examine the behavior of the system in finer and finer granularities. For this model, we asymptotically improve the space bounds of existing work, reduce the update and query time to a constant, and provide deterministic solutions. When evaluated over real Internet packet traces, our fastest algorithm processes items 90--250 times faster, serves queries at least 730 times quicker and consumes at least 40% less space than the best known method.

Algorithms and combinatorial properties on shortest unique palindromic substrings

A palindrome is a string that reads the same forward and backward. A palindromic substring P of a string S is called a shortest unique palindromic substring (SUPS) for an interval [s,t] in S, if P occurs exactly once in S, this occurrence of P contains interval [s,t], and every palindromic substring of S which contains interval [s,t] and is shorter than P occurs at least twice in S. The SUPS problem is, given a string S, to preprocess S so that for any subsequent query interval [s,t] all the SUPSs for interval [s,t] can be answered quickly. We present an optimal solution to this problem. Namely, we show how to preprocess a given string S of length n in O(n) time and space so that all SUPSs for any subsequent query interval can be answered in O(α+1) time, where α is the number of outputs. We also discuss the number of SUPSs in a string.