Wikan Sunindyo

Bayu Hendradjaya

G A Putri Saptawati

Tricya E Widagdo

Document Tracking Technology to Support Indonesian Local E-Governments

Keywords: Document Tracking, E-Government, Business Process, Information and Communication Technology 1

Currently, many information and communication technologies have been used to support electronic government systems (e-gov) to become more effective and efficient. However, in the practical level, some specific issues are still needed to be handled, for example how to manage, handle and track electronic documents in the government institutions, which also can support frequent business process modification. In this paper, we propose to integrate document tracking technology into e-government business process to improve efficiency and effectiveness of e-government application in Indonesia. We offers three integrated generic models of a document tracking system. The models has been applied at a pilot project in an administration office and a city that is enthusiastic to apply a complete e-gov system. We expect that the solution approach can be applied in other local e-governments in Indonesia. Initial results show that the document tracking prototype application can enhance the productivity, clear and simplify the business process, and support process measurement, such that it can be used to improve the quality of local e-government services.

Introduction

To accelerate and expand economic development in Indonesia, the Indonesian government has launched many research and development programs and used many technologies including Information and communication technologies (ICT) to achieve the goal, especially in bureaucratic reformation. The Indonesian bureaucratic reformation should follow these principles, namely [START_REF]BPPT: IT Master Plan Pemerintah Kota Banda Aceh 2010 -2014[END_REF] to create effective bureaucracy that can organize people and support business sector requirements, [START_REF] Hai | Fundamental of Development Administration[END_REF] to create a strong and effective bureaucracy institution, by making clean administration, responsible legislation, and independent judicial institution, (3) to create a good governance application commitment, and (4) to create feedback channel for the future planning [START_REF]Kementerian Koordinator Bidang Perekonomian: Masterplan Percepatan dan Perluasan Pembangunan Ekonomi Indonesia (Masterplan for Acceleration and Expansion of Indonesia Economic Development[END_REF].

To support these bureaucratic reformation programs, a document tracking system is proposed to facilitate the government work process in different sectors and layers.

Currently there are some challenges in processing documents in the governmental institutions, such as:

(1) It is not easy to track the status of some documents/requests. Currently in the location of our case study, there is no document tracking system, thus document finding and tracking is done manually. (2) Bottlenecks in the system are undetected. The work process is not measured properly, therefore the bottlenecks cannot be spotted and the process may take longer than expected. (3) Delayed requests cannot be resolved immediately. Unclear workflow [START_REF] Sunindyo | Workflow Validation Framework in Distributed Engineering Environments[END_REF],

manual process and undocumented delegation cause the requests frequently postponed and cannot be resolved immediately. This is due the fact that there are unclear regulations and slow decision making process in the bureaucracy.

In this paper, we propose to integrate document tracking technology into the egovernment business process. By using document tracking systems, it is expected that the e-government implementation can be achieved more efficiently and effectively. The document tracking system supports faster delivery of the documents and easier handling of documents.

We have developed a prototype of document tracking system and use Payakumbuh city government as our pilot project of document tracking system implementation. Payakumbuh was chosen because the mayor is keen to have an E-Government implementation.

The initial results show that the prototype can help the bureaucracy in finding and tracking the documents, thus it can enhance the productivity, clear and simplify the business process, and support process measurement, such that it can be used to improve the quality of local e-government services.

The remainder of this paper is structured as follows. Section 2 presents related work. Section 3 identifies the research questions. Section 4 describes the solution approach. Finally, section 5 concludes and identifies the future work.

Related Work

This section discusses definition of information and communication technology and related works on e-government and document tracking.

Information and Communication Technology

Information and communication technology (ICT) is a big umbrella of terminology used for covering all technical devices to process and convey information.

ICT includes two different aspects, namely information technology and communication technology. Information technology covers all about how to process information, the utility of information as a tool, and how to manipulate and manage infor-mation, while communication technology involves all about how to use tools to process and transfer data from one device to others. That's why we can't separate those two concepts [START_REF]KPDE-Kampar: Laporan Akhir Penyusunan Master Plan E-Government Kabupaten Kampar (Final Report of E-Government Master Plan Building for Kampar Regency)[END_REF].

ICT Development Principles

Development and implementation of ICT in the government are based on these principles [START_REF]BPPT: IT Master Plan Pemerintah Kota Banda Aceh 2010 -2014[END_REF].

a. Human Resources Quality Improvement Principle

The development and implementation of information technology should be able to strengthen and improve human resources quality, internally and externally.

b. Synergy Principle

The development and implementation of information technology should be able to integrate all available information in the government effectively to support decision making process. The standardization of data and information between institutions is required to support this synergy principle.

c. Utility Principle

The development and implementation of information technology should become more efficient, economical, and effective. The system should be able to deliver information faster, more accurate and on time so it can be used for decision making.

d. Flexibility Principle

The development and implementation of information technology should be done in modular and incremental way to guarantee the flexibility of the systems to adapt internal and external changes.

e. Security and Reliability Principle

The reliability of information technology development and implementation should be guaranteed, such that it can be used anytime. The security and confidentiality of the data should follow the rules and regulations.

f. Legality Principle

The development and implementation of information technology should obey the law, i.e. respect the intellectual property right, including copyrights and other rights that are protected by the laws.

g. Equality of Access Rights Principle

The development and implementation of information technology should guarantee and provide the equality of access rights to governmental information which is open to the public. This principle is intended to avoid digital gaps in certain regions or society.

h. Open Systems, Open Source and Legal Software Principle

The development and implementation of information technology is using open system standard, such that it can integrate different technologies efficiently. The government is expected to use open source application to increase efficiency, economic value to investment, and avoid absolute dependency to certain companies and support IGOS (Indonesia, Go Open Source) movement. If the government uses proprietary applications, it should consider legality aspects [START_REF] Sunindyo | Towards a smart world class city -Case: Building Bandung ICT master plan[END_REF].

E-Government

E-Government (electronic government, digital government, online government, or connected government) consists of the digital interactions between a government and citizens (G2C), government and businesses/Commerce (G2B), government and employees (G2E), and also between government and governments /agencies (G2G). Essentially, the e-Government delivery models can be briefly summed up as [START_REF] Hai | Fundamental of Development Administration[END_REF] (1) G2C (Government to Citizens)

(2) G2B (Government to Businesses) (3) G2E (Government to Employees) (4) G2G (Government to Governments)
This digital interaction consists of governance, information and communication technology (ICT), business process re-engineering (BPR), and e-citizen at all levels of government (city, state/province, national, and international) [9].

Document Tracking

In products distribution and logistics, the product tracing and tracking should consider the process of tracking the location of certain item or property (including other information), for example documents.

Some researchers have been developing document tracking systems as follows.

(1) Schick and Ruland [START_REF] Schick | Document Tracking -On the Way to a New Security Service[END_REF] proposed a new security services that provides reliable technologies for traitor tracing (2) Solic et al [START_REF] Solic | ROADS: RFID Office Application for Document tracking over SIP[END_REF] proposed an RFID-based location tracking system as the next generation communication services in business processes for distributed offices

Research Issues

The research issues are defined as follows.

(1) How to integrate document tracking system into the e-government business process, including the governmental organization, its environmental interaction, the workflow and rules. (2) How to model the document tracking system and implement it.

Proposed Solution

This section presents answer to the research issues mentioned in Section 3. We design the document tracking system first and then implement the prototype of it.

Document Tracking System Modeling

We found that there are at least two categories of document that should be tracked in a organization.

(1) A document approval tracking: A document that is sent by a user to an organization and this document need to get back to this user after a specific process is performed.

(2) A letter or document delivery: An incoming letter or document need to be enrouted to the right person or unit. The letter or document may be originated from internal or external sources.

A document approval tracking is needed when somebody requests an approval from a department body. A person can submit an application document for approval, and a department or unit should examine this document finally give an approval or disapproval. While waiting for the approval process, this person should be able to track his application. This model gives the benefits that the applicant can always check the progress of his/her online application. In addition to that, at the same time people from the department could be encouraged to process the application faster.

An incoming documents or letters are sorted, and sent to the person in charge to handle the documents. The incoming documents sometimes are in the form of a mail letter. The documents sometimes are routed from high level of management hierarchy to lower level. We classify the routing process into two types:

(1) A document or letter that needs a response from a destination unit back to the origin unit or other specific unit

(2) A document or letter that does not need a response.

In both cases, the origin unit usually needs to know that the letter or document is actually reached its destination. The system need to show that the document or letter is actually received. The sender of the documents or letters may come from external parties or from internal unit. The sender unit may need to be able to track down the documents.

Therefore each document should be marked with a unique identification. By using this identification, we can always trace the location of documents. A visualization of this tracking is preferred method to help trace these documents. A notification system can also be used to inform the source if needed.

A visual interaction is a benefit, and we propose that it is a must to have a visual interaction for both parties. A screen can display the units involved and also the current location of the documents.

A user who submits a document can visually track the document through his browser, or by accessing company's special application. The management of an organization can make sure that each documents or letters are being taken care of by a unit. 2 show an interaction's model between the document tracking system and its environment. The sources of documents can be from different units (unit 1, unit 2, unit 3, and so on). This model is designed to be generic so that both types of documents uses the same model. Basically the system interacts with units and also the person who is actually part of a unit. The users of the systems are actually can be just a person for Document Approval Tracking or may also be more than one unit for Document/Letter Delivery System.

Database Requirement Model

The documents need to be recorded in a good database structure. The record needs two kinds of media data, namely as a binary image and as metadata of the document. As a binary image, the scan image of the actual documents (or letters) will be provided. A high speed scanner may need to be presented to scan such documents. The metadata of the document is needed for unique identification and also storing other important information (the incoming date, the sender information, and the receiver information or also the current unit).

The model should also include information of the unit position in the organization structure. The unit position is important for enrouting a letter to the right unit. For example if a document is addressed to the organization, the document may have to be enroute the high level of management first, before it is handed into lower level unit that is responsible to handle such document (or letter).

The model should be flexible enough to be used in different kind of organizations. The model should help the application developer to define the unit structures. The unit structures can be modified by just recording the new data, without changing the structure of the database. The document is associated to a unit. A unit is also associated to other units. The other units can be a parent unit or the same level unit, or a children unit. A parent unit has higher hierarchical structure, and the child is a lower unit. For this document tracking model, we propose a simple and generic model that is flexible to be used in different environment. However a further exploration still be needed to enhance this model.

Implementation Model

The system can be implemented in three platforms (Fig 4) Currently, the prototype of the system is already implemented as a web based application and has been introduced and partially tested in Payakumbuh.

Figure 5 shows a one page example of document status tracking screen. After logging into the document tracking system, a user (e.g. a government officer) can check related documents assigned to this user. They can check and review the name, description, time created, creator, status, and notes of the documents. They can also check the detail of the document. Checking into the document's detail produces the status of the documents which can be changed during the document processing (e.g., the document has been approved or rejected).

Summary and Future Work

Information and communication technologies are very useful to solve problems in many organizations, including in the governmental organization, such as Payakumbuh City Government. By using and integrating the document tracking technologies, makes the bureaucracy process more effective and efficient. However, current imple-

Document Data Receiving Process

Validation Notification Visualization mentation model hasn't covered all possible cases of document tracking in complex institution like Payakumbuh City Government. We need to prepare for further exploration and survey after the first introduction and test of the prototype in the city government.

Figure 5. Document Status Tracking Page

In this paper we have presented three models for supporting the document tracking system. The first model presents an interaction between the document tracking system and its environment. The second model offers a generic database requirement model. The last model presents how the implementation should be accomplished. An implementation of a document tracking system employs this generic model into its design. Some non-technical issues in the implementation of the software system were found during our initial survey. The issues were related to the lack of ICT awareness from the local office people who run the department. Many of them knows how to run computer for a standard office need, however to run a customized software system needs a special skill. We recognized that running a system requires qualified users too, thus we expect that training is needed to have a successful implementation of the system.

We also need to invent new indicators to help demonstrate that the business process of an organization is actually improved after applying new tracking system. Thus another works should include defining indicators to help measuring this process. The indicators can be executed first before applying the new system to get an existing process baseline. A second execution should be performed to help evaluating the improvement from the first.

Other future works should include assessment of local governmental rules. Each local government may have slightly different rules on the handling of documents or letters. The model may have to be updated to adopt new rules; however at this stage we believe that the adoption of the model to some specific requirements should not be a difficult task.

Fig 1 .

 1 Fig 1. Context Diagram of The Document Tracking System for Approval Tracking Fig 1. and Fig.2show an interaction's model between the document tracking system and its environment. The sources of documents can be from different units (unit 1, unit 2, unit 3, and so on). This model is designed to be generic so that both types of documents uses the same model. Basically the system interacts with units and also the person who is actually part of a unit. The users of the systems are actually can be just a person for Document Approval Tracking or may also be more than one unit for Document/Letter Delivery System.

Fig 2 .

 2 Fig 2. Context Diagram of The Document Tracking System for Delivery of Documents or Letters

Fig 3 .

 3 shows of this database model.

Fig 3 .

 3 Fig 3. Database Model

( 1 )Fig 4 .

 14 Fig 4. Implementation Model Diagram

Acknowledgements. This work has been supported by Penprinas MP3EI 2013. Thanks to Dirdik ITB and Payakumbuh City Government for supporting data and information for this paper.


Vignesh Raja
email: vigneshrp@cdac.in

P Sivasankar
email: sivasankar@nitt.edu

Modern Framework for Distributed Healthcare Data Analytics based on Hadoop

Keywords: E-Health, Medical Informatics, Hadoop, Map Reduce, Hive, Massive Multivariate Data sets, BigData Analytics

Evolution in the field of IT, electronics and networking resulted in enhancements in connectivity and in computation capabilities. Proliferation of miniaturized devices paved way for Body Area Network. Healthcare systems have been going through cycles of modernization as the advent of IT system in the field of medical sciences. Body Area Network is a network of lightweight wearable sensor nodes that sense human body functions. Modern healthcare informatics systems produce lots of data that emerge from sensors. Even though many healthcare informatics systems exist and produce volume of data, such solutions exist in silos. Existing Healthcare IT systems intend to gather multivariate medical data about the patients by the means of electronic format. They capture multi variant types of data, process and store them in a RDBMS. Inferring knowledge from such systems is tedious. This paper aims at proposing a framework for modernizing the healthcare informatics systems. Proposed framework is based on Apache Hadoop platform which is open source and its implementation is distributed in nature as it is deployable at various healthcare centers in different geographic locations.

Preliminaries

Health Informatics systems

Health informatics is a multidisciplinary field that deals with computer science, information science, biology, medicine and analytics. Evolution in the electronics, IT field fostered various health informatics solutions. Almost all of the healthcare centers are equipped with health informatics solutions [1] [13]. Such solutions generally consists of modules like hospital information management system, telemedicine solutions and others. These systems basically collect data about the patients and medication in text format, also they store x-rays, scan reports in image format. Such implementations eventually result in "data silo". Data captured by each of the system is local to that environment and they became less reusable. The more the number of health in-formatics solutions the more is the data they produce. Appropriate mechanism is needed in order to store, process and extract knowledge from the massive amount of data. Exchange of vital medical practices and medication assistances through the present healthcare informatics systems have the following concerns  Ability to exchange the data externally  Issues related to data integration standards  Veracity of the healthcare data.

As a health informatics solution, system should provide mechanism for better understanding of medication compliance. Huge amount of money is spent wastefully in healthcare across worldwide is attributed due to excess hospitalization which is triggered by non-compliance of medication procedures. Such incidents are also caused by situations where it lacks of clinically proven medication solutions.

Body Sensor Networks

As the miniaturized sensors emerge as the promising method of self-care medication, establishment of Body Area Network or Body Sensor Network is feasible [START_REF] Ullah | A Comprehensive Survey of Wireless Body Area Networks[END_REF][2]. The purpose of "Body Sensor Networks for Healthcare" is to bring the expertise of doctors in the urban areas closer to the patients in the rural areas. It would bridge geographical distances and provide healthcare to those who do not have access to quality healthcare. More than 70 % of the land area is covered with GSM/GPRS for Internet access [2]. Integration of existing healthcare IT system with wearable health monitoring system is niche in the healthcare IT solutions [START_REF] Ashraf Darwish | Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring[END_REF]. One of the major limitations for wider acceptance of such system was non-existent support for massive data collection and knowledge discovery. Implementation of BSN for healthcare is carried out through infrastructure like medical Kiosk that can enable any person to walk into the kiosk and check the vital parameters like ECG, Blood Pressure, and Pulse Rate etc. The data measured at these kiosks is stored in the distributed datastore and is made available to the doctors / Paramedics. The data could range from text (health records, reports) to image (x-ray, ECG images). Data gathered at each of the healthcare centers are stored in the relational database management system. With such system we have variety and volume of data.

RDBMS lacks of fault tolerance, linear scalability, processing of unstructured data and it cannot effectively handle high concurrent reading and writing of database [START_REF] Yuan | Massive sensor data management framework in Cloud manufacturing based on Hadoop[END_REF]. Also true decentralized approach is required whereby valuable medication information can be shared. Present health informatics systems do not allow exchange of data or when there is need for "healthcare related knowledge" within from the existing data. Along with the massive multivariate data, a mechanism for inferring knowledge from such data is highly required.

Proposed framework

Implementation of new architecture is needed because of the following reasons, the storage required to store healthcare records for large number of patients may results in terabytes (TB) to petabytes (PB) in a typical distributed environment. Also the new architecture is expected to provide storage of massive structured and unstructured data, parallelism in data processing and high availability with fault tolerance. Performing analytics on the historical data to infer knowledge is also a feature of this framework. Proposed model envisages to infer knowledge from such distributed, multivariate data. This framework enables provisions for "Knowledge on Demand". This framework also have a mechanism to seamlessly interface with the legacy system where from the data is imported to perform analytics. As this framework is built using open source tools, extension of functionalities to suit custom requirements is made possible.

Variety of choices including RDBMS (Postgresql 9.x) with high availability, Pandas the open source Python language based analytics library were considered for performing analytics on the data. Even though the recent versions of Postgresql offer ability to handle large volume of data (up to TBs) [START_REF]Postgresql Wiki[END_REF], a hardware infrastructure of higher configuration is required to support such a high volume of data. Further such RDBMS are prone to mid query faults, whereas the proposed Hadoop based framework offers mid query fault tolerance. Pandas -Python based data analytics library [START_REF]Python data analytics library[END_REF] offers many features such as high performance merging and joining, integrated indexing on the given dataset. But most of the data sets using which the analytics is performed, is distributed in nature. Proposed framework provides mechanism to interface with the data sources and it performs analytics using the commodity hardware. So setting up of such a framework does not require the hardware with higher configuration.

Multi variant types of data generated by the existing BSN framework is stored in RDBMS, which is distributed at many locations. This RDBMS based framework cannot be relied on, when the nature of application is with volume, variety of health care data. It is also very cumbersome to manage such data and to perform analytics on such data to infer knowledge [START_REF] Yuan | Massive sensor data management framework in Cloud manufacturing based on Hadoop[END_REF]. By using Hadoop, Hive existing framework can still be exploited to perform analytics on the data it generates. Hadoop based tool sets are introduced in order to alleviate such limitations.

Following are the few usecases that can be executed using this modern framework. Finding out the age group and demography where a particular disease is spreading. Such information will help the Government in planning and implementing suitable health schemes. When the doctors are dealing with critical medical cases, where immediate medical assistance is required, knowledge inferred using the proposed framework can be used in a near real time (Near real-time does not mean the ondemand data by monitoring the patients, but the provision to assist the users by performing analytics on the existing voluminous multivariate datasets). Another usecase is the analytics of structured and unstructured data to provide knowledge when doctors search for specific issues. Any kind of data (medical records, CT scans, emails) can processed through this framework and doctors can then extract the information that they need based on specific symptoms. Below is the components of the framework,

Fig. 1. Proposed Framework

Apache Hadoop

Apache Hadoop is a software framework and it processes large data sets across clusters. Such clusters could be on commodity hardware. With MapReduce (the default programming model), the application is divided into many small fragments of work (Jobs), each of which can execute or re-execute on any node in the cluster [START_REF]Apache Hadoop[END_REF]. Jobs submitted to the Hadoop cluster is managed by Job Tracker and Task Tracker. Hadoop is designed to scale up from single servers to thousands of machines, each offering local computation and storage. It also provides a distributed file system, HDFS [START_REF] White | Hadoop: The Definitive Guide[END_REF]. Overall functioning of inferring knowledge (analytics) is shown below,

Fig. 2. Map Reduce Process

Apache Hive and HBase

Hive is a data warehouse infrastructure built on top of Hadoop and it facilitates querying and managing large datasets residing in distributed storage [START_REF]Apache Hive[END_REF]. Proposed framework supports analysis of data from legacy systems, where the data is available in the RDBMS. Hive supports SQL (HiveQL) like interface, which is familiar to the data professionals. HiveQL basically compiled into map reduce jobs. These jobs are then submitted to the Hadoop for further execution. Extension of existing queries in order to be equipped with tailored map reduce scripts is made feasible by HiveQL. Query plan is generated from the HQL scripts through the execution engine that is available in the Hive.

HBase is an open source distributed database. HBase is highly suited for storing non-relational data.

Apache Sqoop and SpagoBI

Apache Sqoop, tool for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases is also used [8] [9] [START_REF] Dobre | Parallel Programming Paradigms and Frameworks in Big Data Era[END_REF]. Sqoop transfers data from data stores like MySQL, Postgresql and Oracle to Hive and HBase. Proposed framework will also contain SpagoBI to offer BI solutions. SpagoBI is an open source business intelligence suite. Apart from performing query and analytics on large volume of heterogeneous, structured and unstructured data, SpagoBI provides mechanism to extract useful information. SpagoBI can extract information from various platforms such as Hadoop, Hive, HBase and Cassandra. SpagoBI analytical engine can produce charts, reports, thematic maps and cockpits from the information retrieved from such platforms [10] [11].

3 Related Work

Web based Health Informatics application

Existing traditional web based health informatics application is used in order to evaluate this framework. Existing application is based on Java Technologies (J2EE, JSP, and Hibernate). This application is capable interfacing with various sensors that are meant to sense various health factors such as heart beat, blood pressure etc. In its aboriginal form this application is in the state of "Data Silo". All the data captured by this application is internal to this application. For the purpose of interfacing with external application this system is enabled with Web Services. But as the size of total data increases there happens performance lagging since the data is maintained in the traditional database (Postgresql). This web based application also captures medication for various symptoms, prescribed by the Physicians. Currently this system lacks the ability to perform analytics (RDBMS systems analytics performance depends on the factors such as types of queries, number of joins and hardware capacity) on the massive datasets. Proposed framework is applied for this scenario with various options and the results are recorded. Performance evaluation on healthcare data analysis using MapReduce and Hive is carried out against the traditional RDBMS system. Experiment results proves the limitations of the RDBMS when it comes to voluminous data to be dealt and eventually RDBMS based solutions fail to perform data analytics. But MapReduce and Hive continue to work for voluminous data whereas Hive has an edge over MapReduce in terms of performance.

As the SpagoBI is highly configurable Business Intelligence suite is made available as the main interface to the underlying computation infrastructure, various health related information (medication, lab reports etc.) can be easily obtained. SpagoBI is highly customizable to display the rate of change in the health information inferred from the data store via dashboards.

Test Environment

The following environment is setup to evaluate this framework, whereas the data for experiment is taken from web based health informatics application. The data is obtained using Sqoop and stored in the HBase datastore. Data from the application is about heart beat rate, blood pressure level. Total size of the data is approximately 200 GB. Even though 200 GB is not a really the large one, sample dataset was generated based on this dataset of size 200 GB to result in more GBs. The existing database based system's performance has come down and the mid query faults brought havoc to the execution of analytics jobs.

We have used four machines with Intel i7 processors (up to 3.70GHz) and each machine is equipped with 8GB of main memory, 750 GB of storage. We have purposely used commodity hardware to exploit the features of Hadoop. Out of these four machines one machine is configured ad Name Node, two machines for data storage and on the fourth machine the application is configured. This system is set with default replication factor (3 replicas) that is configured in the Hadoop environment. We have experimented this setup with operations like uploading of data and querying of data. Data on the aforementioned health parameters for about 75 patients is uploaded to the system. The system is simulated (based on the sample data) to produce as many records as it requires for conducting the comparison. Heart beat rate of a patient for a day is initially queried, also we queried the number of days when the heart beat rate exceeds certain threshold value. We ended up this experiment analyzing the age group of patients who are likely to get higher heart beat rates. From the table above it is evident that the performance of proposed framework is stable even when the number of record increased drastically whereas the performance of the RDBMS based system is lagging as the data size increases. The sample dataset used for this application consists of data such as blood pressure level, heart beat rate, age and the disease along with other information. In order to infer knowledge from such datasets, queries are constructed and these queries are execued both on the RDBMS also on the proposed framework. Hadoop based framework converts the the given task into Map-Reduce tasks and executes these tasks on the nodes. We have inferred knowledge (finding out the age group and variation in the heart beat rate due to a disease) in the form of a triplet, {Age Group,Heart Beat Rate,Disease}. The more is the details of the inferrence so is the complexity of queries to be constructed. For inferring such knowledge we have used the Select, Aggregation and Join queries.

It is observed that the RDBMS based system failed as we increase the size of datasets also the execution involved in in may sub queris and joins. The next experiment that we performed is to evaluate the performance of MapReduce and that of Hive. Basically this involves quering the massive database thereby to arrive at knowledge from the datastore. Aforementioned health data is again used for this purpose. One intersting phenomenon observed out of this experiment is that the performance of Hive proved to be better than that of the MapReduce. When the same experiment is repeated on the archived data of various intervals, and as the data size increases Hive had offered better performance than that of MapReduce jobs. RDBMS cannot be applied in situations like analyzing the archived data of massive data size. Such analysis is required to infer knowledge as we have indicated few usecases in section 2.

Conclusion

In this paper capabilities of exisitng healthcare informatics systems is addressed. Also the need for decentralized parallel architecture for processing and extracting knowledge out of the datasets is highlighted. For this purpose a modern framework for processing and inferring of voluminous multivariate data is proposed. The proposed framework is fully based on open source tools which provides options for customization and really suits the existing distributed architecture. Also we highlighted the performance evaluation of this framework against RDBMS based data storage. This framework can be configureable on any commodity hardware because of the inherent fault tolerance mechanisms provided by the Hadoop environment.

Table 1 .

 1 Hadoop vs RDBMS

	The results of this experi-


James W Baurley

Bens Pardamean
email: bpardamean@binus.edu

Anzaludin S Perbangsa

Dwinita Utami

Habib Rijzaani

Dani Satyawan

A Bioinformatics Workflow for Genetic Association Studies of Traits in Indonesian Rice

Keywords: data analysis, workflow, agriculture genetics, genome-wide association study, bioinformatics, statistical genetics

Asian rice is a staple food in Indonesia and worldwide, and its production is essential to food security. Cataloging and linking genetic variation in Asian rice to important traits, such as quality and yield, is needed in developing superior varieties of rice. We develop a bioinformatics workflow for quality control and data analysis of genetic and trait data for a diversity panel of 467 rice varieties found in Indonesia. The bioinformatics workflow operates using a back-end relational database for data storage and retrieval. Quality control and data analysis procedures are implemented and automated using the whole genome data analysis toolset, PLINK, and the [R] statistical computing language. The 467 rice varieties were genotyped using a custom array (717,312 genotypes total) and phenotyped for 12 traits in four locations in Indonesia across multiple seasons. We applied our bioinformatics workflow to these data and present prototype genome-wide association results for a continuous trait -days to flowering. Two genetic variants, located on chromosome 4 and 12 of the rice genome, showed evidence for association in these data. We conclude by outlining extensions to the workflow and plans for more sophisticated statistical analyses.

Introduction

Indonesia is located in one of the most biodiverse regions in the world. Studying the biodiversity unique to this region for agriculturally important species can lead to crop and animal improvements. Oryza saliva or Asian rice is a staple food in Indonesia and worldwide, and its production is essential to food security. Cataloging and linking genetic variation in Asian rice to important traits, such as quality and yield, is needed to develop new varieties of rice with superior properties.

The 389 Megabase (Mb) Asian rice genome consist of 12 chromosomes [START_REF]International Rice Genome Sequencing Project: The map-based sequence of the rice genome[END_REF]. Throughout the genome, sequence variations called single-nucleotide polymorphism (SNP) are common. At these locations (or loci), the alternative nucleotides are called alleles, and the two alleles from the paired chromosomes are called SNP genotypes. High-throughput genotyping and sequencing technologies have revolutionized agriculture genetics, allowing for genome-wide interrogation of thousands of SNPs. Recent research using these technologies, have focused on genome-wide genotyping of a rice diversity panel consisting of 413 varieties from 82 countries [START_REF] Zhao | Genomewide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa[END_REF]. While this research has identified genetic regions associated with many complex traits, there is still much to learn about the genetics of rice varieties specific to Indonesia.

The Indonesian Center for Agricultural Biotechnology and Genetic Research and Development (ICABIOGRAD) has developed an unique rice diversity panel of 467 rice varieties found in Indonesia. The panel was planted in a greenhouse (BG) with controlled environment and three fields at different elevations, located in the cities of Citayam, Subang, and Kuningan. The rice was planted in multiple seasons. The diversity panel was genotyped with two panels of 384 and 1,536 SNPs on the GoldenGate platform (Illumina, Inc). The rice was also extensively phenotyped at each location (see Table 1). Given the complexity and volume of the data collected, center researchers needed an efficient and easy system to manage these data and perform numerous genetic association analyses by traits and location.

We designed and implemented a custom bioinformatics workflow for the genetic association study of these traits in Indonesian rice. In the next sections, we present the workflow design, the specifics of the implementation, and prototype results.

Bioinformatics Workflow

We constructed a workflow that captures the bioinformatics needed for data quality control and analysis for both genotypes and phenotypes (trait) (Figure 2.1). Quality control procedures were designed to process the panel of 467 rice varieties captured across the four locations and the genetic data generated from the 384 and 1,536 genotyping arrays. The output of the quality control steps were cleaned data ready for downstream statistical modeling. These datasets were inputs to multi-step analyses pipelines. The output were summary tables and figures of statistical association (Figure 2.1). The workflow was implemented using a combination of software tools and custom programming that included a relational database, the whole genome association analysis toolset PLINK [START_REF] Purcell | PLINK: a tool set for wholegenome association and population-based linkage analyses[END_REF], and the [R] statistical language [START_REF] Development | R: A Language and Environment for Statistical Computing[END_REF].

Rice Relational Database

The bioinformatics workflow operates on a backend relational database for data storage and retrieval. We selected PostgreSQL as a database management system (DBMS) because it is open source and well known for its security, scalability, and active developer community. The entity-relationship diagram for the rice database is presented in Figure 2.2. The database consists of three schemas containing the plant genotypes and trait characteristics. This included genotypes from the 1,536 and 384 SNP arrays from ICABIOGRAD and comparative data from the International Rice Research Institute (IRRI). The snp map table describe the SNPs contained on the array, such as where (chromosome and position); the polymorphic nucleotides -adenine (A), cytosine (C), thymine (T), and guanine (G); and attributes of the array design. The sample map table contains data on the DNA samples and links to the trait data. The final report contains the genotypes for all the samples as well as information on the quality of the genotype calling. Trait data is stored in the phenotype table and linked to the sample map by a one-to-many relationship.

The primary key for final report is the combination of sample index and snp index and dramatically improves the speed of sample based data retrieval (i.e., queries by sample). A second index for final report with the order of the columns reversed allows for quick retrieval of SNP-based queries (e.g., genotypes for particular SNPs). Once the genotype data is imported into the database, the data can be extracted (in whole or by subsets) using Structured Query Language (SQL). This allows for sophisticated filtering and quality control. For the array data, the genotypes are exported from the database into PLINK and converted to binary format for improved performance. The genotype call rate (i.e., the rate of non-missing genotypes) for SNPs and samples are computed and removed if less than 75%. The minor allele frequency (MAF), the frequency of the least common allele, are computed and SNPs with a MAF < 0.05 are flagged as rare variants. When samples were duplicated, genotype concordance is computed to identify SNPs that were not consistently called by clustering algorithms.

Plots are created to evaluate if the clustering algorithm was correctly assigning genotypes. A database query retrieves the r, theta, allele1 ab, and allele2 ab columns from the final report table for particular SNPs. The polar intensities (r, theta) are plotted and compared to the called genotypes AA, AB, BB. When there are genotype misclassifications or missingness, further investigation into the clustering algorithm and assumptions are needed. The quality control steps yield cleaned datasets ready for statistical analyses.

Data Analysis

Descriptive statistics for each trait are generated in [R] and stratified by location and season/year. Descriptive statistics for continuous variables are expressed as mean, median, standard deviation, and ranges. Analyses of continuous variables are performed using t-test or an analysis of variance (ANOVA), as appropriate. Discrete variables are expressed as frequencies and percentages. The analyses of discrete variables are performed using the appropriate chi-squared test. Fishers exact test are used for small cell sizes (< 5). For the phenotypes, all tests of significance are two-tailed, with statistical significance set at p < 0.05.

Principal components analysis (PCA) was implemented in [R] to correct for stratification in the rice diversity panel [START_REF] Price | Principal components analysis corrects for stratification in genome-wide association studies[END_REF]. The top principal components are used as covariates in regression modeling.

The workflow uses generalized linear models (GLM) to model the relationship between traits and each of the 1,536 genotypes. This analysis is stratified by location and season/year. The data D contain the trait variable Y and a matrix of P explanatory variables X (which included each SNP and the top principal components as covariates). The expected value of Y i , the trait variable for rice variant i, depends on the linear predictors through the link function g such that,

g(µ i ) = β 0 + P p β p X ip I p ( 1
)
where µ i = E(Y i ), β p is the regression coefficient of variable p, and I p is a variable indicating if X p is included in the model M . The genotypes are coded additively 0, 1, or 2 for the number of minor alleles. For continuous traits, the identity link function (i.e., linear regression) is used. For binary traits, the logit link function is used, g(π i ) = log πi 1-πi (i.e., logistic regression). The workflow concludes with summarizes of the results obtained from previous steps. Quantile-quantile (QQ) plots are generated for each trait to compare the observed p-value distribution to the expected uniform distribution. Additionally, Manhatten plots are generated to show the p-value results of each association scan by rice chromosome, with p-values less than the user-defined genome-wide threshold for statistical significance highlighted.

Prototype Results

The rice database consists of 17 tables in three schemas, the 1,536 and 384 SNP arrays from ICABIOGRAD and the IRRI 384 SNP array as an standard for assessment. The size of database is 280 Megabytes (Mb). The bioinformatics workflow (Figure 2.1) was run on the largest genotyping array (1,536 SNPs). With the entire diversity panel genotyped, there were 717,312 genotypes in the final report.

A PLINK file was generated and quality control was performed. 16 rice samples and 139 SNPs were removed with poor call rates (< 75%). 451 samples and 1397 SNPs were available for statistical analysis. Principle components (PC) analysis was performed using all SNPs, and the first four PCs were included in model 1 as covariates.

The days to flowering trait was used for prototyping. A summary of the distribution for this trait by location is presented in Figure 3. The box plot shows that there is variation in this trait by location. A genome-wide association analysis was performed on days to flowering for the rice planted in the greenhouse (BG). The resulting Manhattan plot for the association scan is presented in Figure 3. The -log 10 p are presented along the y-axis and the position of the SNP along the 12 rice chromosomes are presented on the x-axis. Two SNPs showed evidence for association with days to flowering in the greenhouse rice (colored red). These SNPs were located on chromosome 4 and 12 of the rice genome (Figure 3). For the top associated SNP, the polar intensities were plotted versus the called genotypes. Intensities with the homozygous genotypes AA and BB were colored in red and green respectively. Heterozygous genotypes AB were plotted in blue. Uncalled genotypes were plotted in black. The graphic illustrates that for this SNP there was a distinct cluster for homozygous genotypes (AA and BB) and a large cluster for heterozygous genotypes (AB). There were 12 samples with no genotype for this SNP.

Conclusions

We developed a custom bioinformatics workflow for a genome-wide association study of traits in Indonesian rice. The workflow consisted of a relational database and multi-step quality control and data analysis procedures automated in PLINK and [R]. The prototype results demonstrated that the workflow is useful for summarizing and visualizing the complex data from this study. Future work includes configuring the workflow to run for all traits, locations, and seasons. Additionally, improvements to the clustering algorithm and statistical modeling framework may be made. Given the small sample sizes in this study and that rice is highly homozygous, an alternative algorithm such as ALCHEMY may improve genotype calling [START_REF] Wright | ALCHEMY: a reliable method for automated SNP genotype calling for small batch sizes and highly homozygous populations[END_REF]. Model 1 may be extended to account for the relatedness among the rice varieties using a mixed model [START_REF] Yu | A unified mixed-model method for association mapping that accounts for multiple levels of relatedness[END_REF]. Environmental factors include the habitat, location, season, and year are possible modifiers of the relationship between genetics and traits. The statistical framework can be modified to consider gene-environment interactions (GxE). This bioinformatics workflow gives researchers the tools needed to easily and consistently quality control and analyze complex data. This research will help locate genes that are important for developing new rice varieties that ensure future food security in Indonesia.

Acknowledgements

Fig. 1 .

 1 Fig. 1. Bioinformatics workflow of rice genetic and phenotypic data

Fig. 2 .

 2 Fig. 2. Rice genetic and phenotypic database

Fig. 3 .

 3 Fig. 3. Boxplot for days to flowering by location (n=467)

Fig. 4 .

 4 Fig. 4. Genome-wide association results for days to flowering, greenhouse

Fig. 5 .

 5 Fig. 5. Clustering for top associated SNP

  This research is funded by the Indonesian Center for Agricultural Biotechnology and Genetic Resources Research and Development, Indonesian Agency for Agricultural Research and Development, Ministry of Agriculture, Indonesia. Computing support by AWS in Education Grant award.

Table 1 .

 1 Rice complex traits measured on 467 rice varieties in 4 locations

	Trait	Units	Description
	Days to flowering		days after planting when 50% of the plants have flowers
	Days to harvest		days after planting until physiological maturity
	Total tiller		number of tillers per hill
	Productive tiller		number of tillers that produce panicles
	Plant height	cm	measured from the ground to the base of the panicle, at
			the time of flowering
	Total panicle		panicles in a square meter
	Panicle length	cm	main stem panicle length, measured from the base to the
			tip of the panicle, 7 days after anthesis.
	Filled grain		average number of filled grain clumps per panicle
	Unfilled grain		average number of empty grain clumps per panicle
	Grain per panicle		total number of grain per panicle
	1000 grain weight gr	weight of 1000 full grain
	Yield	t/ha	tons of rice per hectare