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Starting with multi-source heterogeneous data

The data owned by the financial institutes, which accounts for only a small fraction of all available data, is structured data, whereas such data as for business administration registration, tax administration data, property rights, and lawsuits is unstructured data. It is distributed within government organizations, registration centers and courts and is in various formats. It is rapidly diversifying in types, exponentially increasing in volume, and becoming ever more difficult to use.

Such data is of the same importance for the institutes’ financial judgment. While using the data,they pay no attention to whether the data is saved in rows, columns, or raw texts but lays mush emphasis on how the data can help them reach their goals and cope with their challenges. They need a way to ask questions to their data and receive clear and simple responses, which will be perfectly satisfied by HIGGS KUNLUN.

Integration of Data and Finance

The world’s top experts in credit risk and market risk have been invited to make an integration of the multi-source heterogeneous data and financial risk models so as to produce a model with an ever wider coverage, a more reasonable weight and is more capable of the quantification of risks.

As large amounts of data flows into HIGGS KUNLUN, they are transformed into meaningfully defined objects and relationships: people, locations, things, events, and the relationships between them.

Tag and track all the data

Once the model has been determined, the data will be continuously input into HIGGS KUNLUN. Through the information security system, users who are even in the same organization cannot get access to the information without authorization.

All updates or changes of sources will be automatically recorded in the platform. And users’ operations will be automatically tracked, categorized and recorded for improvement and reutilization based on their habits.

Bring the data to life for investigation and analysis

Users can do data analyses through their applications on the platform of HIGGS KUNLUN. They can search all data sources at once, visualize the relations, verify their hypotheses, discover hidden relations and patterns and share their insights with their colleagues.

By building a connection between users and data, HIGGS KUNLUN helps financial institutes comprehensively improve their capability of risk discovery and evaluation.

Platform

On the back-end, HIGGS KUNLUN comprises a complete set of capabilities for the integration of data from different sources to perform complex analyses. The platform, like a financial risk data base, includes all kinds of data and analyses on credit risk.

平台_灵活的模型配置

Flexible model configuration

Different from traditional data processing in lines or columns, HIGGS KUNLUN can forge a connection between people and organizations with their own features through flexible configuration. The data model, through quick definition and redefinition, is able to integrate multi-source heterogeneous data into a uniformed system that helps users to dig target information.

平台_灵活的模型配置
平台_隐私与安全
平台_隐私与安全

Privacy and security

The privacy strategy, established in the underlying architecture of HIGGS KUNLUN, is able to support highly accurate access controls, multi-level security strategies and complete auditability. First, all attributes of the targets in the platform can be linked to the data sources and thus the setting of access limits can be specified to all attributes.
And with diversified access controls, users can better manage the used data. Last but not the least, the operations of all users and administrators will be recorded in the unchangeable audit logs.

平台_合作

Cooperation

With t HIGGS KUNLUN, users of the same or different organizations can make seamless and secure cooperative analyses of the same data. The platform supports trans-organizational, trans-industrial and even trans-regional cooperation; also it supports trans-security and trans-data models, and keeps secure and complete data even in the low bandwidth and highly delayed network environment.

平台_合作
平台_可扩展、自定义和API
平台_可扩展、自定义和API

Extensibility, customizability and API

All levels and modules of HIGGS KUNLUN re designed to be extensible. As an open platform, it includes the low-level data integration, customized importing methods and customized user interfaces. Access to the integrated data is possible with API.

平台_知识管理

Knowledge management

Everything is stored in HIGGS KUNLUN, no matter it is linked to the updating of data sources or users’ operations. Users can store the data and analysis models in their own accounts, which allow other users’ authorized access and further optimization.
Users can explore the analysis models built on different ideas, make records and analyses of all steps and track back to any part of the investigations. Analyzers can share their conclusions without loss of their achievements. Then comes a knowledge base with the function of version control, which, through the transformation of the users’ analyses accumulated into data, will play a more important role in the future.

平台_知识管理
平台_算法处理
平台_算法处理

Algorithm processing

By automatic identification of aggregates of data for analyzers’ inspection, the algorithm integrated in the platform greatly improves users’ processing of large-scale data. HIGGS KUNLUN, in favor of the integration and analysis of large-scale data sets, supports a strong and flexible automatic processing framework. Non-technical analyzers can work through the graphical processing surface without any code writing.cal processing surface without any code writing.

平台_超大规模数据处理

Ultra-large-scale data processing

With the extensible data structure and corporate data storage, HIGGS KUNLUN can easily process PB-Level data. The data warehouse can store large-scale structured data sets, such as logs, network flow and exchange data. Corporate data can store large-scale unstructured data sets, such as files, emails and messages.
The above two storage technologies both support high-performance searching and other searching methods. Data inquiry will be automatically input in the platform for analysis.

平台_超大规模数据处理

Application

HIGGS KUNLUN provides a series of integrated analyzingtools on its front end, including semantic, time series, geographic location and textual analyses. Users could make analysis with several applications without loss of existed analyses.

应用_图形

Graphs

Graphic application provides a visual exploration of the relations between data targets, which are tagged as nodes and sides.
Users can discover what they are interested in with filters. A graphic application includes a timeline tool for event sequences, a time roller for event cycles and frequencies, an integrated choosing and screening attribute-specific tool and a visual tool for monitoring the data flowing such as communication, payment and logistics.
Users can analyze the hierarchical relations and other network features by adjusting the presentation of nodes and sides in the graphic application, which at the same time helps analyzers improve and explain the produced graphs with a presentation assistant tool.

应用_图形
应用_地图
应用_地图

Map

The map application can make analyses based on the geographic locations. A timeline and time roller visual tool inside can choose and screen the geographic locations on the map, while a thermodynamic chart tool can present the density of targets on the map.
The changeable presentations enable users to swift between different methods, embed their own and also create new ones according to the data features.
KML and shape files can be input as independent map layers, while the input shapes can be used in choosing and screening other targets in similar areas. Layers can be colored and tagged based on the data computations in the layers.

应用_对象浏览器

Target browser

Users can get a large number of data sets with the target browser. Supported by HIGGS KUNLUN, analyzers can define and apply a series of data target filters to get data subsets with the target browser and make further analysis with other applications such as graphs and maps.

应用_对象浏览器
应用_数据浏览器
应用_数据浏览器

Data browser

Users can read and apply both structured and unstructured files in the data browser. When reading the original texts, users can link a specific phrase with its corresponding target in the corresponding database by a tag, which can be further used in other applications.

应用_移动应用

Mobile application

The mobile application provides real-time and distributed cooperation and data collection and therefore is suitable for mobile office work. Analyzers on the spot can establish real-time cooperation with the head-quarter through the mobile application. Users can thereby fill investigation reports, upload photos and videos, track team members’ locations and search the integrated data in the platform.

应用_移动应用

Technology

技术_冰川,结构化数据储存方案

Iceberg, structured data storage solution

Iceberg, a minute-level inquiry and block storage solution with trillions of records and PB-level data, makes advanced analysis of large-scale data with several open source technologies.

技术_冰川,结构化数据储存方案
技术_青鸟,非结构化数据储存方案
技术_青鸟,非结构化数据储存方案

Blue Bird, unstructured data storage solution

Blue Bird provides integrated search solutions of external data sources and integrates data with the OTF solution. The searching results discovered by an integrated inquiry will be immediately sent to the data base.

技术_搜索

Search

The searching of HIGGS KUNLUN supports the textual inquiry of all data including structured and unstructured data inside the system.

技术_搜索
技术_帕米尔,内存储存数据库
技术_帕米尔,内存储存数据库

Pamirs, internal memory storage database

Pamirs, as an internal memory database technology of HIGGS, supports the interaction with large-scale data. With technology as such, billions of targets can be searched in ten seconds. Established in 2009, Pamirs has a design similar to Spark. With the target browser driven by Pamirs, analyzers can pick out an operable and valuable subset from large amount of data for further analysis.

技术_璞玉,灵活配置、基于对象的数据模型

Jade, target-specific data model with flexible configuration

Jade, a target-specific data model with flexible configuration of HIGGS, transforms and integrates date of different sources in original formats into target data and attributes which can reflect people, locations, things, events and relations in a real society. Due to different institutions’ different understandings and the gradual evolution of analysis models, Jade will make specific models for different cases and update them according to the source changes. After the flexible and uniform model processing, the data integration will be easy and as short as only several weeks.

技术_璞玉,灵活配置、基于对象的数据模型
技术_归墟,数据仓库
技术_归墟,数据仓库

Ray, data warehouse

Ray, an eternal data warehouse of HIGGS KUNLUN, drives the access control, auditing, knowledge management, cooperation functions of the platform. All data of Ray includes its own historical information, such as creating and modifying dates, creators and modifiers, sources of derivation and other security and access limits.Users can get analyses of rich texts through direct access to the information and establish cooperation with users of different access limits and modeling logics. Extensible information includes security and version controls and can help users make analyses of different angles and different periods on the premise of complete data.

技术_山海,归墟的数据储存容器

Shanhai, data storage container of Ray

Shanhai, as the storage container of Ray, combines the simplicity and extensibility of the currently distributed NoSQL as well as the safety and consistency of the traditional SQL database. Shanhai supports all compatible “key-value” storage on its top. As a newly designed API of high configuration, Shanhai, from desk level to data center level, obeys linear changes in its performance.

技术_山海,归墟的数据储存容器
技术_Eureka,跨域合作
技术_Eureka,跨域合作

Eureka, trans-regional cooperation

Eureka network is a distributed system of HIGGS KUNLUN and can create a duplicate of HIGGS KUNLUN for every organization customer. Every duplicate operates above the uniform public data and the data owned by the organizations themselves. With authorization, two different organizations’ Eurekas can establish data and analysis cooperation. Eureka supports various kinds of Jade and various access control strategies, and at the same time ensures the consistency of different duplicates. Eureka network will support the trans-organizational, trans-functional and trans-regional security data sharing and cooperative data analyses.