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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Graphic application provides a visual exploration of the relations between data targets, which are tagged as nodes and sides.
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.
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.
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.
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.
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.
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.
The searching of HIGGS KUNLUN supports the textual inquiry of all data including structured and unstructured data inside the system.
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.
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.
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.
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.
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.