Wei He

54 papers A* 3A 7B 2C 6Journal 15Unranked 21
YearRankTypeTitle / Venue / Authors
2025 J jnl
A Survey on Federated Recommendation Systems.
IEEE Trans. Neural Networks Learn. Syst.
Zehua Sun, Yonghui Xu, Yong Liu, Wei He, Lanju Kong, Fangzhao Wu, Yali Jiang, Lizhen Cui
2025 J jnl
A capsule-based reinforcement learning framework for supply-demand matching in mobile crowdsourcing.
Expert Syst. Appl.
Yiping Li, Wei He, Lizhen Cui, Yonghui Xu, Wei Guo, Zhen Feng
2025 A conf
DAG-AFL: Directed Acyclic Graph-based Asynchronous Federated Learning.
ICME
Shuaipeng Zhang, Lanju Kong, Yixin Zhang, Wei He, Yongqing Zheng, Han Yu, Lizhen Cui
2025 J jnl
DAG-AFL:Directed Acyclic Graph-based Asynchronous Federated Learning.
CoRR
Shuaipeng Zhang, Lanju Kong, Yixin Zhang, Wei He, Yongqing Zheng, Han Yu, Lizhen Cui
2025 B conf
EDGM: Efficient and Dynamic Generative Model for Dataset Distillation.
PRICAI
Gaoyuan Ma, Yonghui Xu, Wei He, Lizhen Cui
2025 J jnl
Exploring the Frontier of Generative AI: Evolution, Applications, and Future Trends.
Int. J. Crowd Sci.
Xiaofang Sun, Wei He
2025 — conf
Meta Relation Assisted Explanatory Model for Heterogeneous Graph Neural Networks.
DASFAA (3)
Yibowen Zhao, Yonghui Xu, Yu Zhang, Qingzhong Li, Xudong Lu, Wei He, Lizhen Cui
2024 C conf
AutoMP: A Tool to Automate Performance Testing for Model Placement on GPUs.
ISPA
Zhe Yang, Wei He, Fenglong Cai, Yonghui Xu, Wei Guo, Lizhen Cui
2024 J jnl
FastPTM: Fast weights loading of pre-trained models for parallel inference service provisioning.
Parallel Comput.
Fenglong Cai, Dong Yuan, Zhe Yang, Yonghui Xu, Wei He, Wei Guo, Lizhen Cui
2024 — conf
Multi-objective Graph Neural Network Explanatory Model with Local and Global Information Preservation.
DASFAA (6)
Yibowen Zhao, Yonghui Xu, Yixin Zhang, Wei He, Lizhen Cui
2023 — conf
A Comprehensive Review of the Oversmoothing in Graph Neural Networks.
ChineseCSCW (1)
Xu Zhang, Yonghui Xu, Wei He, Wei Guo, Lizhen Cui
2023 J jnl
A Survey on Federated Recommendation Systems.
CoRR
Zehua Sun, Yonghui Xu, Yong Liu, Wei He, Lanju Kong, Yali Jiang, Fangzhao Wu, Lizhen Cui
2023 J jnl
An reinforcement learning approach for allocating software resources.
Concurr. Comput. Pract. Exp.
Xiaojing Yin, Jiwei Huang, Lei Liu, Wei He, Lizhen Cui
2023 — conf
Brain Functional Residual Temporal Convolution Network for Major Depressive Disorder Recognition.
BIBM
Xiaofang Sun, Wei He, Yonghui Xu, Yali Jiang, Xiangwei Zheng, Yongqing Zheng, Wei Guo, Lizhen Cui
2023 A* conf
Cross-Domain Disentangled Learning for E-Commerce Live Streaming Recommendation.
ICDE
Yixin Zhang, Yong Liu, Hao Xiong, Yi Liu, Fuqiang Yu, Wei He, Yonghui Xu, Lizhen Cui, Chunyan Miao
2023 J jnl
Modeling Long- and Short-Term User Preferences via Self-Supervised Learning for Next POI Recommendation.
ACM Trans. Knowl. Discov. Data
Shaowei Jiang, Wei He, Lizhen Cui, Yonghui Xu, Lei Liu
2023 A conf
ParaTra: A Parallel Transformer Inference Framework for Concurrent Service Provision in Edge Computing.
ICWS
Fenglong Cai, Dong Yuan, Mengwei Xie, Wei He, Lanju Kong, Wei Guo, Yali Jiang, Lizhen Cui
2023 — conf
Towards Distributed Graph Representation Learning.
ChineseCSCW (1)
Hanlin Zhang, Yue Zhang, Wei He, Yonghui Xu, Lizhen Cui
2022 A conf
Balancing Supply and Demand for Mobile Crowdsourcing Services.
ICSOC
Zhaoming Li, Wei He, Ning Liu, Yonghui Xu, Lizhen Cui, Kaiyuan Qi
2022 J jnl
CK-Encoder: Enhanced Language Representation for Sentence Similarity.
Int. J. Crowd Sci.
Tao Jiang, Fengjian Kang, Wei Guo, Wei He, Lei Liu, Xudong Lu, Yonghui Xu, Lizhen Cui
2022 A conf
Combining User Inherent and Contextual Preferences for Online Recommendation in Location-Based Services.
ICSOC
Haiting Zhong, Wei He, Lizhen Cui, Lei Liu
2022 A* conf
Enhancing Sequential Recommendation with Graph Contrastive Learning.
IJCAI
Yixin Zhang, Yong Liu, Yonghui Xu, Hao Xiong, Chenyi Lei, Wei He, Lizhen Cui, Chunyan Miao
2022 J jnl
Enhancing Sequential Recommendation with Graph Contrastive Learning.
CoRR
Yixin Zhang, Yong Liu, Yonghui Xu, Hao Xiong, Chenyi Lei, Wei He, Lizhen Cui, Chunyan Miao
2022 A* conf
FeedRec: News Feed Recommendation with Various User Feedbacks.
WWW
Chuhan Wu, Fangzhao Wu, Tao Qi, Qi Liu, Xuan Tian, Jie Li, Wei He, Yongfeng Huang, Xing Xie
2022 J jnl
Joint Attention Networks with Inherent and Contextual Preference-Awareness for Successive POI Recommendation.
Data Sci. Eng.
Haiting Zhong, Wei He, Lizhen Cui, Lei Liu, Zhongmin Yan, Kun Zhao
2022 J jnl
Stationary response determination of MDOF fractional nonlinear systems subjected to combined colored noise and periodic excitation.
Commun. Nonlinear Sci. Numer. Simul.
Fan Kong, Huimin Zhang, Yixin Zhang, Panpan Chao, Wei He
2022 — conf
Temporal Hypergraph for Personalized Clinical Pathway Recommendation.
BIBM
Fanglin Zhu, Shunyu Chen, Yonghui Xu, Wei He, Fuqiang Yu, Xu Zhang, Lizhen Cui
2021 J jnl
Achieving Approximate Global Optimization of Truth Inference for Crowdsourcing Microtasks.
Data Sci. Eng.
Lizhen Cui, Jing Chen, Wei He, Hui Li, Wei Guo, Zhiyuan Su
2021 J jnl
Behavioral data assists decisions: exploring the mental representation of digital-self.
Int. J. Crowd Sci.
Yixin Zhang, Lizhen Cui, Wei He, Xudong Lu, Shipeng Wang
2021 J jnl
Group task allocation approach for heterogeneous software crowdsourcing tasks.
Peer-to-Peer Netw. Appl.
Xiaojing Yin, Jiwei Huang, Wei He, Wei Guo, Han Yu, Lizhen Cui
2021 — conf
Personalized Clinical Pathway Recommendation via Attention Based Pre-training.
BIBM
Xijie Lin, Yuan Li, Yonghui Xu, Wei Guo, Wei He, Honglu Zhang, Lizhen Cui, Chunyan Miao
2020 — conf
A Pruned DOM-Based Iterative Strategy for Approximate Global Optimization in Crowdsourcing Microtasks.
APWeb/WAIM (1)
Lizhen Cui, Jing Chen, Wei He, Hui Li, Wei Guo
2020 B conf
An Iterative Feedback Mechanism for Auto-Optimizing Software Resource Allocation in Multi-Tier Web Systems.
CCGRID
Xiaojing Yin, Jiwei Huang, Lei Liu, Wei He, Lizhen Cui
2020 A conf
Answer Aggregation for Crowdsourcing Microtasks using Approximate Global Optimal Searching.
ICWS
Lizhen Cui, Jing Chen, Wei He, Wei Guo
2020 — conf
D-colSimulation: A Distributed Approach for Frequent Graph Pattern Mining based on colSimulation in a Single Large Graph.
SCC
Guanqi Hua, Junhua Zhang, Lizhen Cui, Wei Guo, Xudong Lu, Wei He
2020 — conf
Multi-interest Aware Recommendation in CrowdIntell Network.
ISPA/BDCloud/SocialCom/SustainCom
Yixin Zhang, Wei He, Lizhen Cui, Lei Liu, Zhongmin Yan
2020 — conf
Predicting Hospital Readmission Using Graph Representation Learning Based on Patient and Disease Bipartite Graph.
DASFAA (2)
Zhiqi Liu, Lizhen Cui, Wei Guo, Wei He, Hui Li, Jun Gao
2020 — conf
Task Assignment with Consensus Decision for Spatial Crowdsouring.
ISPA/BDCloud/SocialCom/SustainCom
Xiaomeng Lian, Wei He, Lizhen Cui, Hui Li, Lei Liu
2019 C conf
A Dynamic Difficulty-Sensitive Worker Distribution Model for Crowdsourcing Quality Management.
CollaborateCom
Miao Zheng, Lizhen Cui, Wei He, Wei Guo, Xudong Lu
2019 — conf
Value-Oriented Ranking of Online Reviews Based on Reviewer-Influenced Graph.
DASFAA (3)
Yiming Cao, Lizhen Cui, Wei He
2018 — conf
Answer Aggregation of Crowdsourcing Employing an Improved EM-Based Approach.
ICA3PP (3)
Ran Zhang, Lei Liu, Lizhen Cui, Wei He, Hui Li
2018 — conf
Spatial Crowdsourcing Task Assignment Based on the Quality of Workers.
ICCSE
Yun Jiang, Lizhen Cui, Yiming Cao, Lei Liu, Wei He, Li Pan, Yongqing Zheng, Qingzhong Li
2018 — conf
Spatial Task Allocation Based on User Trajectory Prediction.
ChineseCSCW
Yun Jiang, Wei He, Lizhen Cui, Qian Yang, Zhaohui Peng
2018 — conf
Task Assignments in Complex Collaborative Crowdsourcing.
ChineseCSCW
Wei He, Lizhen Cui, Cheng Huang
2018 A conf
User Location Prediction in Mobile Crowdsourcing Services.
ICSOC
Yun Jiang, Wei He, Lizhen Cui, Qian Yang
2016 — conf
Maximizing the Availability of Process Services in Mobile Computing Environments.
SCC
Wei He, Hui Li, Lizhen Cui, Shuoyan Lu
2016 C conf
Quality-Assure and Budget-Aware Task Assignment for Spatial Crowdsourcing.
CollaborateCom
Qing Wang, Wei He, Xinjun Wang, Lizhen Cui
2015 C conf
User Behavioral Context-Aware Service Recommendation for Personalized Mashups in Pervasive Environments.
APWeb
Wei He, Guozhen Ren, Lizhen Cui, Hui Li
2014 A conf
A Context-Based Autonomous Construction Approach for Procedural Mashups.
ICWS
Wei He, Qingzhong Li, Lizhen Cui, Ting Li
2014 C conf
A context-based collaboration supported framework for E-commerce PaaS platform.
CSCWD
Zongshui Xiao, Wei He, Ting Li, Li-zhen Cui
2011 — conf
An Adaptive Distribution Model for Multi-dimensional Data in Decentralized Environments.
WISM (1)
Wei He, Li-zhen Cui
2010 — conf
A Multi-Dimensional Measuring Model for E-travel Service in Decentralized Network.
ICEE
Wei He, Lixin Du, Haiyang Wang, Li-zhen Cui
2010 — conf
An efficient service search strategy in decentralized e-business environments.
NCM
Wei He, Yongqing Zheng, Li-zhen Cui
2007 C conf
A Groupware-supported Workflow Model and its Applications in Electric Power Enterprise.
CSCWD
Wei He, Haiyang Wang, Li-zhen Cui
notebooks/redb-clickhouse-TEST.ipynb
← Index notebooks/redb-clickhouse-TEST.ipynb plaintext
Truncated preview: showing the first 16384 bytes out of 5988848 total.
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import clickhouse_connect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ch_TEST_client = clickhouse_connect.get_client(host='e.rationaledge.io', port=443, username='default', password='werioubTGwvf0-dsbv_', database='redb', verify=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch_TEST_BETA_client = clickhouse_connect.get_client(host='k.rationaledge.io', port=443, username='default', password='werioubTGwvf0-dsbv_', database='default', verify=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method HttpClient.close_connections of <clickhouse_connect.driver.httpclient.HttpClient object at 0x105fd64d0>>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch_TEST_BETA_client.close_connections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<clickhouse_connect.driver.summary.QuerySummary at 0x11f900810>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch_TEST_BETA_client.command(\"\"\"\n",
    "RENAME DATABASE redb TO orig_redb;\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DB Creation (schema 2025-08-02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch_TEST_client.command(\"\"\"\n",
    "CREATE DATABASE IF NOT EXISTS redb;\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define General Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### CREATE FULL REDB DATABASE\n",
    "def create_redb_with_all_tables(client):\n",
    "    client.command(\"\"\"\n",
    "        CREATE DATABASE IF NOT EXISTS redb\n",
    "    \"\"\")\n",
    "    create_all_general_tables(client)\n",
    "    create_all_pe_tables(client)\n",
    "\n",
    "#### CREATE FULL TABLES\n",
    "def create_all_general_tables(client):\n",
    "    create_basic_properties_dbtable(client)\n",
    "    create_hashes_dbtable(client)\n",
    "    create_capa_dbtable(client)\n",
    "    create_die_dbtable(client)\n",
    "    create_extra_findings_dbtable(client)\n",
    "\n",
    "\n",
    "##################################\n",
    "#### CREATE INDIVIDUAL TABLES ####\n",
    "##################################\n",
    "\n",
    "#### GENERAL TABLES ####\n",
    "# All tables have SETTINGS index_granularity = 8192\n",
    "def create_basic_properties_dbtable(client):\n",
    "    # Create samples_metadata table\n",
    "    client.command(\"\"\"\n",
    "        CREATE TABLE IF NOT EXISTS redb_basic_properties (\n",
    "            sha256 FixedString(64), -- primary key\n",
    "            md5 FixedString(32),\n",
    "            sha1 FixedString(40),\n",
    "            filename Nullable(String),\n",
    "            sample_name String,\n",
    "            filesize UInt64,\n",
    "            file_entropy Float64,\n",
    "            filetype LowCardinality(String),\n",
    "            filetype_mime LowCardinality(String),\n",
    "            filetype_magika LowCardinality(String),\n",
    "            analysis_date DateTime64(3, 'UTC'),\n",
    "        ) ENGINE = ReplacingMergeTree(analysis_date)\n",
    "        PRIMARY KEY (sha256)\n",
    "        ORDER BY (sha256)\n",
    "    \"\"\")\n",
    "\n",
    "def create_hashes_dbtable(client):\n",
    "    # Create hashes table\n",
    "    client.command(\"\"\"\n",
    "        CREATE TABLE IF NOT EXISTS redb_hashes (\n",
    "            sha256 FixedString(64), -- primary key\n",
    "            md5 FixedString(32),\n",
    "            sha1 FixedString(40),\n",
    "            ssdeep_hash Nullable(String),\n",
    "            tlsh_hash Nullable(String),\n",
    "            authentihash Nullable(String),\n",
    "            imphash Nullable(String),\n",
    "            impfuzzy Nullable(String),\n",
    "            typerefhash Nullable(String),\n",
    "            richhash Nullable(String),\n",
    "            richpe_hash Nullable(String),\n",
    "            richpv_hash Nullable(String),\n",
    "            richpv_hash_sorted Nullable(String),\n",
    "            dhash Nullable(String),\n",
    "            analysis_date DateTime64(3, 'UTC')\n",
    "        ) ENGINE = ReplacingMergeTree(analysis_date)\n",
    "        PRIMARY KEY (sha256)\n",
    "        ORDER BY (sha256)\n",
    "    \"\"\")\n",
    "\n",
    "def create_capa_dbtable(client):\n",
    "    # Create sample_capabilities table\n",
    "    client.command(\"\"\"\n",
    "        SET enable_json_type = 1;\n",
    "    \"\"\")\n",
    "    client.command(\"\"\"\n",
    "        -- Base table for CAPA analysis results\n",
    "        CREATE TABLE IF NOT EXISTS redb_capa (\n",
    "            sha256 FixedString(64),\n",
    "            md5 FixedString(32),\n",
    "            sha1 FixedString(40),\n",
    "            analysis_date DateTime64(3, 'UTC'),\n",
    "            -- Store full JSON dumps as strings for flexibility\n",
    "            capa_dump JSON CODEC(ZSTD(3)),\n",
    "            attack_dump JSON CODEC(ZSTD(3)),\n",
    "            mbc_dump JSON CODEC(ZSTD(3))\n",
    "        ) ENGINE = ReplacingMergeTree(analysis_date)\n",
    "        ORDER BY sha256;\n",
    "    \"\"\")\n",
    "\n",
    "    client.command(\"\"\"\n",
    "        -- Normalized table for capabilities and namespaces\n",
    "        CREATE TABLE IF NOT EXISTS redb_capa_capabilities (\n",
    "            sha256 FixedString(64),\n",
    "            md5 FixedString(32),\n",
    "            sha1 FixedString(40),\n",
    "            analysis_date DateTime64(3, 'UTC'),\n",
    "            namespace LowCardinality(String),\n",
    "            capability LowCardinality(String)\n",
    "        ) ENGINE = ReplacingMergeTree(analysis_date)\n",
    "        ORDER BY (sha256, namespace, capability);\n",
    "    \"\"\")\n",
    "\n",
    "    client.command(\"\"\"\n",
    "        -- Materialized view for capability statistics\n",
    "        CREATE MATERIALIZED VIEW IF NOT EXISTS redb_capa_capabilities_stats (\n",
    "            namespace LowCardinality(String),\n",
    "            capability LowCardinality(String),\n",
    "            samples_bitmap AggregateFunction(groupBitmap, UInt64),\n",
    "            occurrence_count UInt64\n",
    "        ) ENGINE = AggregatingMergeTree()\n",
    "        ORDER BY (namespace, capability)\n",
    "        AS SELECT\n",
    "            namespace,\n",
    "            capability,\n",
    "            groupBitmapState(cityHash64(sha256)) as samples_bitmap,\n",
    "            count() as occurrence_count\n",
    "        FROM redb_capa_capabilities\n",
    "        GROUP BY namespace, capability;\n",
    "    \"\"\")\n",
    "\n",
    "    client.command(\"\"\"\n",
    "        -- Normalized table for MITRE ATT&CK mappings\n",
    "        CREATE TABLE IF NOT EXISTS redb_capa_attack (\n",
    "            sha256 FixedString(64),\n",
    "            md5 FixedString(32),\n",
    "            sha1 FixedString(40),\n",
    "            analysis_date DateTime64(3, 'UTC'),\n",
    "            tactic LowCardinality(String),\n",
    "            technique LowCardinality(String),\n",
    "            technique_id LowCardinality(String)\n",
    "        ) ENGINE = ReplacingMergeTree(analysis_date)\n",
    "        ORDER BY (sha256, tactic, technique_id);\n",
    "    \"\"\")\n",
    "\n",
    "    client.command(\"\"\"\n",
    "        -- Materialized view for ATT&CK statistics\n",
    "        CREATE MATERIALIZED VIEW IF NOT EXISTS redb_capa_attack_stats (\n",
    "            tactic LowCardinality(String),\n",
    "            technique LowCardinality(String),\n",
    "            technique_id LowCardinality(String),\n",
    "            samples_bitmap AggregateFunction(groupBitmap, UInt64),\n",
    "            occurrence_count UInt64\n",
    "        ) ENGINE = AggregatingMergeTree()\n",
    "        ORDER BY (tactic, technique_id)\n",
    "        AS SELECT\n",
    "            tactic,\n",
    "            technique,\n",
    "            technique_id,\n",
    "            groupBitmapState(cityHash64(sha256)) as samples_bitmap,\n",
    "            count() as occurrence_count\n",
    "        FROM redb_capa_attack\n",
    "        GROUP BY tactic, technique, technique_id;\n",
    "    \"\"\")\n",
    "\n",
    "    client.command(\"\"\"\n",
    "        -- Normalized table for MBC mappings\n",
    "        CREATE TABLE IF NOT EXISTS redb_capa_mbc (\n",
    "            sha256 FixedString(64),\n",
    "            md5 FixedString(32),\n",
    "            sha1 FixedString(40),\n",
    "            analysis_date DateTime64(3, 'UTC'),\n",
    "            objective LowCardinality(String),\n",
    "            behavior LowCardinality(String),\n",
    "            behavior_id LowCardinality(String)\n",
    "        ) ENGINE = ReplacingMergeTree(analysis_date)\n",
    "        ORDER BY (sha256, objective, behavior_id);\n",
    "    \"\"\")\n",
    "\n",
    "    client.command(\"\"\"\n",
    "        -- Materialized view for MBC statistics\n",
    "        CREATE MATERIALIZED VIEW IF NOT EXISTS redb_capa_mbc_stats (\n",
    "            objective LowCardinality(String),\n",
    "            behavior LowCardinality(String),\n",
    "            behavior_id LowCardinality(String),\n",
    "            samples_bitmap AggregateFunction(groupBitmap, UInt64),\n",
    "            occurrence_count UInt64\n",
    "        ) ENGINE = AggregatingMergeTree()\n",
    "        ORDER BY (objective, behavior_id)\n",
    "        AS SELECT\n",
    "            objective,\n",
    "            behavior,\n",
    "            behavior_id,\n",
    "            groupBitmapState(cityHash64(sha256)) as samples_bitmap,\n",
    "            count() as occurrence_count\n",
    "        FROM redb_capa_mbc\n",
    "        GROUP BY objective, behavior, behavior_id;\n",
    "    \"\"\")\n",
    "\n",
    "def create_die_dbtable(client):\n",
    "    # Create sample_die table\n",
    "    client.command(\"\"\"\n",
    "        SET enable_json_type = 1;\n",
    "    \"\"\")\n",
    "    client.command(\"\"\"\n",
    "        CREATE TABLE IF NOT EXISTS redb_die (\n",
    "            sha256 FixedString(64),\n",
    "            md5 FixedString(32),\n",
    "            sha1 FixedString(40),\n",
    "            die_info JSON CODEC(ZSTD(3)),\n",
    "            analysis_date DateTime64(3, 'UTC')\n",
    "        ) ENGINE = ReplacingMergeTree(analysis_date)\n",
    "        PRIMARY KEY (sha256)\n",
    "        ORDER BY (sha256)\n",
    "    \"\"\")\n",
    "\n",
    "    client.command(\"\"\"\n",
    "        CREATE MATERIALIZED VIEW redb_die_mv\n",
    "        (\n",
    "            sha256 FixedString(64),\n",
    "            md5 FixedString(32),\n",
    "            sha1 FixedString(40),\n",
    "            analysis_date DateTime64(3, 'UTC'),\n",
    "            compiler LowCardinality(Nullable(String)),\n",
    "            compiler_full Nullable(String),\n",
    "            format LowCardinality(Nullable(String)),\n",
    "            format_full Nullable(String),\n",
    "            installer LowCardinality(Nullable(String)),\n",
    "            installer_full Nullable(String),\n",
    "            joiner LowCardinality(Nullable(String)),\n",
    "            joiner_full Nullable(String),\n",
    "            language LowCardinality(Nullable(String)),\n",
    "            language_full Nullable(String),\n",
    "            library LowCardinality(Nullable(String)),\n",
    "            library_full Nullable(String),\n",
    "            linker LowCardinality(Nullable(String)),\n",
    "            linker_full Nullable(String),\n",
    "            operation_system LowCardinality(Nullable(String)),\n",
    "            operation_system_full Nullable(String),\n",
    "            packer LowCardinality(Nullable(String)),\n",
    "            packer_full Nullable(String),\n",
    "            protector LowCardinality(Nullable(String)),\n",
    "            protector_full Nullable(String),\n",
    "            sfx LowCardinality(Nullable(String)),\n",
    "            sfx_full Nullable(String),\n",
    "            sign_tool LowCardinality(Nullable(String)),\n",
    "            sign_tool_full Nullable(String),\n",
    "            stub LowCardinality(Nullable(String)),\n",
    "            stub_full Nullable(String),\n",
    "            tool LowCardinality(Nullable(String)),\n",
    "            tool_full Nullable(String),   \n",
    "            -- overlay JSON CODEC(ZSTD(3)),\n",
    "            -- resource JSON CODEC(ZSTD(3)),\n",
    "            has_overlay UInt8,\n",
    "            has_resources UInt8,\n",
    "            is_packed UInt8\n",
    "        )\n",
    "        ENGINE = ReplacingMergeTree(analysis_date)\n",
    "        ORDER BY (sha256)\n",
    "        POPULATE\n",
    "        AS SELECT\n",
    "            sha256,\n",
    "            md5,\n",
    "            sha1,\n",
    "            analysis_date,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'compiler'), '') AS compiler,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'compiler(full)'), '') AS compiler_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'format'), '') AS format,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'format(full)'), '') AS format_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'installer'), '') AS installer,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'installer(full)'), '') AS installer_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'joiner'), '') AS joiner,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'joiner(full)'), '') AS joiner_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'language'), '') AS language,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'language(full)'), '') AS language_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'library'), '') AS library,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'library(full)'), '') AS library_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'linker'), '') AS linker,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'linker(full)'), '') AS linker_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'operation_system'), '') AS operation_system,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'operation_system(full)'), '') AS operation_system_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'packer'), '') AS packer,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'packer(full)'), '') AS packer_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'protector'), '') AS protector,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'protector(full)'), '') AS protector_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'sfx'), '') AS sfx,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'sfx(full)'), '') AS sfx_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'sign_tool'), '') AS sign_tool,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'sign_tool(full)'), '') AS sign_tool_full,\n",
    "            nullIf(JSONExtractString(toString(die_info), 'stub'), '') AS stub,\n",
    "            nullIf(JSONExtractString(toString(die_info),