733 results about "Access frequency" patented technology

The invention relates to the technical field of data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

The invention relates to the technical field of data processing, in particular to a block chain-based data sharing system, which comprises a field path hash index module, a permission entry sorting module, a path compliance judgment module, a certificate state updating and verification module and a joint permission confirmation module. According to the method and the device, the multilevel structure information of the field path chain is extracted from the data sharing calling request, salt coding is performed in combination with the calling party address, and hash processing is performed, so that unique mapping of the field path is realized, field identification fuzziness and path conflict are effectively avoided, and accurate positioning of sharing operation is ensured. By means of path structure analysis and access frequency normalization processing, a field entry weight sequence is established in combination with a scoring mechanism, field access entries can be sorted according to actual use frequency and structure hierarchy, and authorization judgment efficiency is improved. A task responsibility number and a timestamp are extracted from an access behavior track, a responsibility chain segment is established, and abnormal changes in a responsibility path can be effectively recognized.

The invention discloses a data storage control method and device, a storage medium and electronic equipment, and relates to the technical field of computers, and the data storage control method comprises the steps that the access frequency of multiple pieces of data accessed in a future time period is predicted according to historical access characteristics of the multiple pieces of data in a storage system; detecting whether the data access types of the data are changed or not according to the frequency range in which the access frequency falls; under the condition that it is detected that the data access type of the target data is changed, a storage strategy of the target data is adjusted according to the target data access type obtained after the target data is changed, and a target storage strategy is obtained; and executing the target change operation on the target data, the target change operation being used for changing the storage strategy of the target data to the target storage strategy, thereby solving the technical problem that it is difficult for a storage system to consider both storage overhead and storage performance during data storage. The technical effect that the storage cost and the storage performance are both considered when the storage system stores the data is achieved.

The invention relates to the technical field of storage optimization, in particular to a data storage management system and method based on big data, and the system comprises an efficiency monitoring module, a medium evaluation module, a decision generation module, a strategy execution module and an effect feedback module. According to the method, input and output delay, a medium health attenuation curve and a bandwidth fluctuation map are collected, a dynamic feature space is constructed, the stability of the system is judged in combination with a Lyapunov index, early warning of performance abnormity is achieved, health indexes such as an erasing cycle, a seeking error rate and a charge retention rate are fused, and the access frequency and the load pressure are combined. Calculating a compensation coefficient, improving heterogeneous medium cooperation efficiency, pre-migration evaluation cost and topology adjustment income, improving resource configuration accuracy, dynamically adjusting hot and cold data distribution and node load, enhancing system scheduling flexibility, comparing throughput, medium wear rate and synchronous delay, and quantifying stability gain brought by configuration change; and a closed-loop process from identification to feedback is constructed.

The invention provides an unstructured data storage optimization method and system based on information entropy and a block chain, and relates to the technical field of data storage, and the method comprises the steps: building a block chain storage network to obtain data, recognizing redundant data through dimension reduction, mapping and hyperplane construction, and removing the redundant data based on fuzzy similarity. The to-be-stored data is partitioned, the access frequency is calculated, the consistent Hash algorithm is executed to determine the storage position, the access index and the verification sequence are established, and the data integrity verification is executed, so that the data access efficiency can be improved, and the storage security can be enhanced.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention provides a distributed storage method based on source code semantic partitioning, and particularly relates to the technical field of cloud data distributed storage. The method comprises the steps of performing semantic partitioning on a source code, and segmenting the source code into a plurality of semantic blocks according to dimensions such as functional semantics, an abstract syntax tree structure, author information and version information; generating metadata containing information such as grammar type tags, file paths, line number ranges, author identifiers, version identifiers and access popularity for each semantic block; constructing a weighted directed acyclic graph (DAG) based on the semantic chunks and the dependency relationship thereof; superposing a metadata layer in the DAG structure, and recording information such as function call dependency, inter-block reference relationship and version evolution chain; blocks with relatively high access frequency and close semantics are aggregated into super blocks, the traversal depth is reduced, and meanwhile, hot data and cold data are differentiated for hierarchical storage by adopting a cold and hot data management strategy; and evaluating a parent block aggregation degree through a BDS algorithm, determining a block sorting priority, and optimizing super block boundary division. Compared with the prior art, the method has the advantages that the semantic retrieval efficiency, the incremental updating capability and the distributed query performance of the source code storage system are improved.

The invention discloses a multi-level cache optimization method and system based on data popularity self-adaptive adjustment, relates to the technical field of data storage, and aims to solve the problems that a cache mechanism in an existing desktop cloud system is static, initialization is slow, and cold and hot data are difficult to recognize. The adopted scheme comprises the steps that a three-level cache structure of a local cache L1, a distributed cache L2 and a database L3 is set; constructing a data popularity scoring model based on the access frequency, the time decay and the user priority; dividing the data into hot data, temperature data and cold data according to the popularity score, and dynamically determining migration and elimination strategies of the data in L1, L2 and L3; the hot data are loaded to the upper-level cache T in advance through a prediction mechanism, and container-level cache preheating is supported; a cache preheating strategy is integrated to a Kubernetes life cycle, and hot data loading and cold data degradation are achieved; visual strategy configuration and monitoring during operation are supported, and cache strategies are managed in real time. The method is suitable for an access optimization scene of a large-scale desktop cloud system.

The invention belongs to the field of data storage, and discloses a frequency access data storage migration method and device for big data, and the method comprises the steps: obtaining a data access log, carrying out the preprocessing of the data access log, and obtaining a time window statistical matrix; carrying out timeliness attenuation weighting calculation on a plurality of data access times in the time window statistical matrix to obtain a weighted access frequency vector; obtaining a multi-dimensional feature matrix based on the weighted access frequency vector; clustering the feature vector of each time window slice in the multi-dimensional feature matrix through a Gaussian mixture model to obtain a clustering tag vector; inputting the clustering label vector into an access frequency prediction model to obtain an access frequency prediction value of the data in a future preset time step; the migration decision matrix is determined based on the access frequency predicted value and the data storage cost parameter, data migration is carried out based on the migration decision matrix, the data migration complexity can be reduced, and the access speed and the storage cost are balanced.

The invention relates to the technical field of storage, in particular to an SSD wear leveling method and device based on dynamic data migration and a medium. The method comprises the following steps: monitoring access frequency, recording read-write times of a logic block in real time, and identifying hotspot data; tracking a wear state, counting the erasure times of the physical flash memory block, and evaluating the wear degree of the physical flash memory block; data migration decision: determining whether to migrate data based on the access frequency and the wear frequency; preferentially migrating data with high access frequency and high wear degree according to access frequency and wear program sorting; and data migration execution: migrating the hotspot data from the high-wear area to the low-wear area, and updating the mapping relation. According to the method, the data storage position can be intelligently adjusted according to the real-time data access characteristics and the abrasion state, and therefore the service life of the SSD is more efficiently prolonged.

The invention discloses an intelligent data storage method based on a cloud platform, relates to the technical field of data storage management, and is used for solving the problem that cold and hot data identification is lagged. According to the method, the access behavior graph is constructed, the concurrent access coupling weight is calculated, the data object with high access frequency and deep path hierarchy is identified, the structure adjustment instruction is dynamically generated, the access link complexity and index hierarchy redundancy are reduced, the response delay and the popularity prediction error before and after the structure adjustment are combined, and the access efficiency is improved. According to the method, the scheduling efficiency of the hotspot data is improved, the adaptability to the change of a storage access mode is enhanced, the dynamic adjustment capability and the structure response performance are good, and the method can be used for realizing the closed-loop linkage of the access behavior perception, the structure reconstruction and the model optimization. Therefore, the method is more suitable for a large-scale data intelligent management scene under a cloud platform.

The invention relates to the field of data query, and particularly provides a medical big data query method and system based on dynamic desensitization, a terminal and a medium, and the method comprises the steps: collecting multi-source heterogeneous medical data, carrying out semantic normalization, constructing a unique identifier of a patient, and generating standardized intermediate plaintext data; constructing an adaptive isomorphic index structure based on the data, constructing a semantic hash B + tree index for structured data, constructing a feature vector index for unstructured data, and dynamically optimizing index hierarchy and parameters according to access frequency; and after a query request is received, the permission context is analyzed, intermediate plaintext data is positioned and read by using the index, and a differential privacy protection strategy is dynamically applied according to the access permission. According to the method, the problem of balance between privacy protection and data effectiveness as well as between retrieval efficiency and storage cost in medical data processing is effectively solved, and data availability and access performance are improved while safety compliance is guaranteed.

The invention relates to the technical field of distributed storage, in particular to a high-speed distributed storage system and method, and the system comprises a file index characterization module which carries out the statistics of the access frequency and the recent access timestamp of each data block in the system, and obtains the popularity magnitude of the file data blocks; according to the method, the popularity magnitude concept is introduced on the basis of linkage statistics of the data block access frequency and the recent access time, the data blocks with remarkable service influence in the current system can be recognized, the priority processing sequence of different data blocks in the repair process is divided, and the decision-making efficiency during fault recovery is improved. After the to-be-repaired data blocks are identified, the comprehensive weight values are formed through correlation calculation with the popularity magnitude, and all the weight values are arranged in a descending order, so that dynamic grading and sequential scheduling of repair tasks are realized, and important data blocks are guaranteed to preferentially obtain recovery resource support.

The invention relates to the technical field of embedded data storage, and discloses a method and a system for prolonging the service life of a FLASH simulation EEPROM (Electrically Erasable Programmable Read-Only Memory) in a single chip microcomputer, and the method comprises the following steps: firstly, initializing the FLASH, dividing the FLASH into a main data area, a mapping table area, a backup area and a transaction log area, selecting a physical block through a dynamic wear leveling algorithm when data is written, and writing the data into a database; the method comprises the following steps of: starting a backup area or a transaction log area, safely updating a mapping table by using a transaction mechanism, performing cyclic redundancy check when reading data, if the cyclic redundancy check fails, starting cascade recovery from the backup area or the transaction log area, and finally, updating a transaction log to record operation so as to form a closed-loop management process. According to the invention, through a set of dynamic wear leveling and data classification management mechanism, global and uniform distribution of the write load in the storage array is realized, the physical block with the least erasing times is selected during writing, the access frequency of the data can be identified, and cold data which is not changed frequently can be actively migrated.

The invention provides an intelligent archive data sub-library storage system and method based on dynamic fragmentation and self-adaptive indexing, and relates to the technical field of intelligent archive data management. And efficient management of the archive data in storage and access levels is realized. Constructing an archiving attribute tensor matrix, performing popularity grouping according to the access frequency, the reference relationship and the modification frequency, and quickly identifying high-access-value data; then, a life cycle attribute is introduced to construct a stage weight, a fragment structure is dynamically optimized, copy information is updated in real time, and data distribution reasonability and disaster recovery robustness are effectively improved. Meanwhile, the system fuses access path optimization, index hop count compression and caching strategies, self-adaptive adjustment and version solidification of an index structure are achieved by evaluating an index response efficiency index, the average access time is effectively shortened, and the hit rate is improved.

A heterogeneous storage-oriented hot and cold data management method and system, wherein hot and cold data are managed by means of the following steps: at table granularity, storing data from each table into an independent data bucket in the form of object storage, each data bucket comprising a plurality of partitions, the data being randomly distributed in each partition, and the consistency of replicas of the partitioned data being ensured by means of a distributed consensus algorithm; collecting statistics about the access frequencies of the partitions in one data bucket; when the access frequency of one partition reaches a set threshold, adding a new replica to a target storage medium by means of a membership change in the distributed consensus algorithm; and when data synchronization between the new replica and an old replica is completed, detecting the old replica. In this way, the migration of the partitioned data on different storage media is realized, and data cooling and heating are implemented. The method and system can maximize the use efficiency of high-performance storage media, ensure the access efficiency of users, and can reduce the costs of data migration and maintenance.

The invention discloses a storage and calculation separation method and device based on multi-level cache and intelligent scheduling and a server, and belongs to the technical field of data processing, and the method comprises the following steps: collecting operation behavior data of a user, carrying out distributed storage, and dynamically distributing and calculating node cache space; performing classification marking to form marked user data; performing intelligent scheduling layer analysis tasks on the marked user data, and screening and distributing adaptive computing nodes; the computing node receives the analyzed task to check the local cache, obtains corresponding data from the local or request remote storage according to the validity of the local corresponding data, computes the corresponding data, and caches the computed data to the local or remote storage according to the user access probability; and carrying out dynamic scheduling and hierarchical storage on the calculated data between local cache and remote storage according to access frequency and heat factors. According to the invention, the problems of low transmission efficiency and unbalanced resource utilization of the existing storage and calculation separation architecture are solved.

The invention relates to the technical field of data storage, in particular to a mass data multilayer hierarchical storage method fusing access frequency and life cycle weight, which comprises the following steps: calculating the value life cycle weight of a data object and the physical life cycle health degree of a target storage medium in parallel, and outputting a composite weight factor through a coupling function; calculating a net benefit score value based on the composite weight factor and the data real-time access frequency in combination with a migration I / O loss estimated value of cross-layer migration; when the net benefit score value breaks through a preset migration threshold value, data migration is executed, and physical storage positions are dynamically distributed on the target storage layer according to the physical life cycle health degree. According to the method, the migration threshold is dynamically adjusted through the business key factors, the core data migration responsiveness is guaranteed, and meanwhile, high-cost migration behaviors are prevented from being triggered by temporary or edge data by mistake.

The invention discloses a memory access method and device, a storage medium and a program product, and relates to the technical field of memory access, comprising: monitoring memory access information of a target memory; the memory access information comprises a missing page address sequence during memory access, an address distribution characteristic when an address conversion failure event occurs in an address conversion lookaside buffer, a cross-node access event and a virtual memory access frequency; determining corresponding target feature information; the target feature information comprises spatial locality, thermal density, access dispersion and unbalance degree data; and determining a current memory access mode based on the target feature information, and determining a target access strategy from preset memory access strategies configured with different memory page table prefetching rules to access the memory. The memory access feature information is quantified through the memory access information, the corresponding memory access mode is determined, and the corresponding memory page table prefetching rule is executed, so that the traversal delay of the multi-level page table can be reduced, and the memory access performance is improved by fully utilizing the memory bandwidth.

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

The invention belongs to the technical field of storage media, and particularly relates to a storage medium abrasion balancing system based on cold and hot data perception, which comprises a cold and hot data perception module used for monitoring the access frequency and the update time of data in real time so as to judge the cold and hot degree of the data, and establishing a heat table, recording a heat value of each data block or data page; and the data feature prediction module is used for predicting features of a future heat change trend and a data volume increase trend of the data by utilizing a machine learning algorithm according to historical data access and update records. According to the method, the hot and cold data sensing module is used for monitoring data access and updating conditions in real time to calculate a hot value, and the storage medium management module is combined to preferentially store hot data into blocks with less erasing times and store cold data into blocks with more erasing times. Therefore, the erasing times of the blocks of the storage medium tend to be balanced, and excessive abrasion of individual blocks is avoided.

The invention discloses an online incremental learning method and system based on a self-adaptive B + tree index, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an extended self-adaptive B + tree index structure, inserting a new sample, and calculating a trajectory disturbance value; generating a disturbance score by combining the disturbance value and a node state, and adding a new sample and an associated node sample into an incremental learning scheduling queue when a condition is met; when a node splitting, merging or reconstructing event occurs, acquiring an affected node and updating a structural code of the affected node; monitoring sample label track change, adjusting a label confidence mark when a threshold value is exceeded, and limiting participation frequency according to a screening mechanism; and performing forgetting compression operation on the leaf nodes with low access frequency and small label fluctuation degree. By constructing an adaptive B + tree index and combining disturbance-driven scheduling, structure change response and forgetting compression strategies, sample management refinement, learning update high efficiency and structure maintenance controllability are realized, and the stability and resource utilization efficiency of online incremental learning are improved.

The invention discloses an IDC-oriented multi-source heterogeneous data real-time hierarchical storage method, and relates to the technical field of data storage, an IDC platform is constructed, resource pools are established in the IDC platform, the resource pools comprise high-performance resource pools and low-performance resource pools, and allocation conditions are respectively set corresponding to the high-performance resource pools and the low-performance resource pools; setting a resource configuration assistant corresponding to the resource pool in the IDC platform; and establishing a connection relationship between the IDC platform and a multi-source data end, collecting corresponding data through the multi-source data end, and storing the data in a main resource plate and a resource pool. According to the method and the device, the data can be stored in a hierarchical manner according to the accessed frequency of the data, the hierarchical storage comprises storage in a high-performance resource pool and storage in a low-performance resource pool, and the corresponding memory can be allocated according to the accessed frequency of the data, so that the use of the high-performance memory can be saved.

The invention discloses a deep network security data protection strategy optimization method fused with behavior analysis, which relates to the technical field of network security data protection, and comprises the following steps of: converting a resource identifier, an access timestamp and an access frequency of each access behavior into three-dimensional quantitative vectors by constructing an access behavior time mapping table; carrying out aggregation matching on the access behaviors with the same resource identifier and the access frequency lower than a set threshold value in different time periods, and determining multiple low-frequency interleaving access behavior characteristics occurring for the same resource object in a discontinuous time period; and constructing a cross-cycle risk assessment path with continuity and context relevance by using time sequence backtracking, a resource object merging strategy and a context semantic nesting mechanism according to the determined multi-time low-frequency interleaving access behavior characteristics. According to the method, the problem that low-frequency interlaced attack behaviors are difficult to identify is solved, and cross-cycle risk modeling and intelligent optimization of a protection strategy are realized.

The invention discloses an equipment access method, device and equipment based on address space identification. The method is applied to simplifying an equipment memory management unit and comprises the steps of establishing each single-level range table; acquiring an equipment access request, reading an address space identifier and a virtual address from the equipment access request, and matching a corresponding target range table from each single-level range table based on the address space identifier; and calculating a target physical address according to the target range table and the virtual address, and establishing equipment access based on the target physical address. By replacing a traditional multi-level page table with a single-level structure, the data structure design of address mapping is simplified, the dynamic configuration complexity is reduced, and hardware formal verification is facilitated. Through direct matching of the address space identifier, the traditional steps of analyzing the equipment identity identifier and querying the external table item are omitted, and the external memory access frequency is reduced. By calculating the target physical address and establishing access, the hardware address conversion logic is simplified, the hardware implementation cost and power consumption are reduced, and the data transmission efficiency is improved.

The invention belongs to the technical field of machine room power distribution, and provides a machine room power distribution equipment remote monitoring method and system, and the method comprises the steps: collecting operation data in real time through deploying edge equipment, fusing basic operation data, transient feature data and historical abnormal data, and constructing a state scoring model; utilizing a neural network training weight coefficient to realize equipment health degree dynamic evaluation; the sampling frequency is adaptively adjusted based on a scoring result, abnormal data are screened by combining multiple characteristic thresholds such as current sudden change intensity, energy sudden change quantity and temperature gradient, and only high-frequency data fragments in an abnormal time period are reserved; through an edge-cloud collaborative storage mechanism, abnormal data are locally cached after RAID redundancy check, normal data are uploaded to the cloud according to storage duration and access frequency in a grading manner, full-life-cycle traceability of the data is realized through tagging processing, anomaly detection sensitivity is improved, and storage efficiency and fault diagnosis reliability are both considered.

The invention provides an instruction execution method, a processor, electronic equipment and a storage medium, the method is applied to the processor, the processor comprises a first execution unit, a second execution unit and a pipeline register, and a first execution instruction of an atomic instruction group is obtained through the first execution unit; writing a first execution result of the first execution instruction into the pipeline register in response to the first execution instruction; a second execution instruction of the atomic instruction group is obtained through the second execution unit, and the first execution result is read from the pipeline register as the source operand of the second execution instruction in response to the second execution instruction, so that the instruction execution method, the processor, the electronic equipment and the storage medium can reduce the access frequency of the general register and improve the access efficiency of the general register. Processor power consumption and register port conflicts can be reduced.

The invention discloses an auditing data security management and control system based on big data, and relates to the field of data management and control, and the system comprises a distributed crawler which collects auditing logs, operation records and network traffic in real time. And the stream batch integrated engine performs metadata alignment and timestamp synchronization on heterogeneous data to generate a unified audit event stream. The intelligent desensitization module carries out gradient desensitization on the sensitive fields and constructs reversible blood relationship mapping. According to the hierarchical storage, data are stored in an encrypted memory pool, an SSD and a cold standby cluster according to data sensitivity and access frequency, and consistent hash load balancing is achieved. Multi-modal safety monitoring analyzes data in real time through a three-level assembly line. And the hierarchical response module writes a permission chain according to the risk trigger action and the key audit event hash value, and constructs a tamper-proof evidence chain. The method has the advantages that the data security, privacy protection and efficient emergency response are ensured through intelligent desensitization, hierarchical storage and security analysis of the audit data, and the non-tampering property of the data is ensured by utilizing the block chain technology.

The invention discloses a log grading method and device, equipment and a program product, and the method comprises the steps: obtaining access parameters of a to-be-graded log, and the access parameters comprise at least two of the number of access users, the access frequency and the access timeliness; determining a comprehensive grading index of the to-be-graded log according to the access parameter; determining a target dynamic grading threshold according to historical log access data in the target first sliding time window; and determining a target grading result of the to-be-graded log according to the target dynamic grading threshold value and the comprehensive grading index. Access parameters of multiple dimensions are considered when the comprehensive grading index is determined, and the limitation that traditional grading depends on a single index is solved. Besides, the sliding time window is dynamically changed, so that dynamic grading of the to-be-graded logs is realized, and the problem that static grading cannot dynamically adapt to user access requirements is solved.

The invention relates to the technical field of distributed computing, and discloses a distributed computing modeling system design method for high-dimensional discrete data, which comprises the following steps: acquiring a feature identifier access request in a high-dimensional discrete data stream, counting an access frequency, and marking a feature identifier of high-frequency access as a high-frequency access state; constructing a local parameter copy on a non-master node physical computing unit of the distributed cluster; routing gradient update data aiming at the feature identifier to a local parameter copy closest to the network and generating a local residual tensor; calculating the direction cosine similarity of the local residual tensor and the global gradient update vector; calculating a real-time access entropy based on the access source distribution and converting the real-time access entropy into a dynamic synchronous threshold value; according to the method, through a dynamic synchronization judgment mechanism based on semantic information gain, high-frequency but invalid redundant communication is effectively inhibited, and network congestion under long-tail distribution is reduced.