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RFID data managememt

Seven Principles of Effective RFID Data Management.pdf 216KB

Data Management Challenges in RFID - Persistent Systems.pdf 421KB

8.Temporal Management of RFID Data.pdf 1.03MB

RFID Data Management.pdf 146KB

RFID Data Management Challenges and Opportunities.pdf 314KB

Temporal Management of RFID Data

Fusheng Wang Peiya Liu

Integrated Data Systems Department

Siemens Corporate Research

755 College Road East, Princeton, NJ 08540, USA

{fusheng.wang, peiya.liu}@siemens.com

Abstract

RFID technology can be used to signiﬁcantly im-

prove the efﬁciency of business processes by pro-

viding the capability of automatic identiﬁcation

and data capture. This technology poses many

new challenges on current data management sys-

tems. RFID data are time-dependent, dynamically

changing, in large volumes, and carry implicit se-

mantics. RFID data management systems need

to effectively support such large scale temporal

data created by RFID applications. These sys-

tems need to have an explicit temporal data model

for RFID data to support tracking and monitor-

ing queries. In addition, they need to have an

automatic method to transform the primitive ob-

servations from RFID readers into derived data

used in RFID-enabled applications. In this pa-

per, we present an integrated RFID data manage-

ment system – Siemens RFID Middleware – based

on an expressive temporal data model for RFID

data. Our system enables semantic RFID data

ﬁltering and automatic data transformation based

on declarative rules, provides powerful query sup-

port of RFID object tracking and monitoring, and

can be adapted to different RFID-enabled applica-

tions.

1 Introduction

1.1 Background

RFID (radio frequency identiﬁcation) technology uses

radio-frequency waves to transfer data between readers and

movable tagged objects, thus it is possible to create a physi-

cally linked world in which every object is numbered, iden-

tiﬁed, cataloged, and tracked. RFID is automatic and fast,

Permission to copy without fee all or part of this material is granted pro-

vided that the copies are not made or distributed for direct commercial

advantage, the VLDB copyright notice and the title of the publication and

its date appear, and notice is given that copying is by permission of the

Very Large Data Base Endowment. To copy otherwise, or to republish,

requires a fee and/or special permission from the Endowment.

Proceedings of the 31st VLDB Conference,

Trondheim, Norway, 2005

and does not require line of sight or contact between read-

ers (or sensors) and tagged objects. With the signiﬁcant

advantages of RFID technology, RFID is being gradually

adopted and deployed in a wide area of applications, in-

cluding supply chain management [1, 2, 3, 4, 5], retail [6],

anti-counterfeiting [7], security [8], and healthcare. For ex-

ample, Siemens Business Services launched a pilot project

to track patients with RFID bracelets during hospital ad-

missions [9].

Through the automatic data collection, RFID technol-

ogy can achieve greater visibility and product velocity

across supply chains, more efﬁcient inventory manage-

ment, easier product tracking and monitoring, reduced

product counterfeiting and theft, and much reduced labor

cost. On the other hand, there is a chasm between the phys-

ical world and the interpreted world through sensor obser-

vations. These observations need to be automatically in-

terpreted and semantically transformed into business logic

data, before they can be integrated into business applica-

tions, such as ERP and WMS.

1.2 Characteristics of RFID Data and Problem State-

ments

Despite the diversity of RFID applications, RFID data

share common fundamental characteristics, which have to

be fully considered in RFID data management systems.

Temporal and dynamic

. RFID applications dynamically

generate observations and the data carry state changes.

All sensor observations are associated with the timestamps

when the readings are made; objects’ locations change

along the time; the containment relationships change along

the time, and all EPC [10] related transactions are also as-

sociated with time. (EPC – Electronic Product Code – is

an identiﬁcation scheme for universally identifying physi-

cal objects, deﬁned by standard committees [10].) It is es-

sential to model all such information in an expressive data

model suitable for application level interactions, including

tracking, monitoring, and application integration [11, 12].

Implicit semantics and inaccuracy of data

. In an RFID

system, a reader observation comprises of the reader EPC,

the observed EPC value of an RFID tag, and the timestamp

when the observation occurs. These data carry implicit in-

formation, such as changes of states and business processes

1128

(e.g., change of locations), and further derived information

such as aggregations (e.g., containment relationship among

objects). Thus, a framework is needed to automatically

transform observations into business logic data.

Meanwhile, while the accuracy of current RFID readers

is improving, in current RFID applications, there are still

erroneous readings, such as duplicate readings or missing

readings. Such erroneous data have to be semantically ﬁl-

tered.

Streaming and large volume

. RFID data are generated

quickly and automatically, and accumulated for tracking

and monitoring. The data volume generated can be enor-

mous, which requires a scalable storage scheme, to assure

efﬁcient queries and updates.

Integration

. RFID data have to be integrated with exist-

ing applications for product tracking and monitoring. This

requires an RFID data management system be easily con-

ﬁgured to be integrated into different applications, with

minimum integration cost.

In early RFID solutions, sensor readings are directly

sent to applications and services, thus it is up to the appli-

cations to interpret the preliminary readings, and generate

business logic data. This approach has much complexity on

RFID data interpreting, and is not scalable and adaptable.

Recently, major IT vendors are moving quickly to pro-

vide RFID solutions [13, 14, 15, 16, 17]. These sys-

tems provide sophisticated middleware-based platforms for

RFID applications, and serve as bridges between the phys-

ical RFID world and the rest software infrastructure. High

level data modeling, on the other hand, is up to applica-

tions.

1.3 Our Approach to RFID Data Management

In this paper, we propose an expressive temporal-based

data modeling of RFID data, using a Dynamic Relationship

ER Model (DRER). By maintaining the history of events

and state changes, the data model captures the fundamental

RFID application logic into the data model itself, thus com-

plex queries can be easily supported. With a rules-based

framework, observations can be automatically transformed

into business logic data, through user conﬁgured rules. By

bringing all the technologies together, we build Siemens

RFID Middleware, an integrated RFID data management

system. Salient features of our approach include:

–

Expressive Data Model

. DRER is built on ER model,

with minimum extensions. The semantics of the data

model is generalized from RFID data, thus ﬁts per-

fectly with RFID data.

–

Effective Query Support

. Based on the temporal data

model, common RFID queries such as RFID object

tracking and monitoring, and other complex queries,

can be effectively supported. Moreover, partitioning-

based archiving assures efﬁcient updates and queries.

–

Automatic Data Acquisition and Transformation

With a framework of rules-based transformation, the

system achieves automatic data ﬁltering and transfor-

mation, with minimum conﬁguration.

–

Adaptable and Portable

. The business-logic integrated

data model and the rules-based data transformation

can be adapted to different RFID applications, and

substantially reduce the cost of managing RFID data

and integrating RFID data into business applications.

This paper is organized as follows. In Section 2, we pro-

pose the Dynamic Relationship ER Data Model for RFID

data, then we show in Section 3, how the data model can

beneﬁt queries in an RFID data management system. In

Section 4, we discuss how to semantically process RFID

data and automatically transform RFID data into business

logic. Then, in Section 5, we show how to efﬁciently sup-

port queries and updates through partitioning-based archiv-

ing. Next in Section 6, we discuss Siemens RFID Middle-

ware that integrates all the technologies into an adaptable

system. Section 7 discusses experiments and Section 8 dis-

cusses related work. Section 9 concludes the paper.

2 RFID Data Modeling

2.1 Motivation

A Sample RFID-enabled Application. Figure 1(a) shows

a simpliﬁed example of an RFID-enabled supply chain sys-

tem. In a supplier warehouse, each product item is EPC-

tagged. (Here we assume products are tagged at case level.

Our methods can be generalized to any tagging level.) The

tagged cases are packed onto a pallet at the supplier ware-

house (location L001), where the EPC tags of both the

cases and the containing pallet is automatically scanned

by an RFID reader R1. (Here we assume all RFID tags

are passive tags, which are the cheapest and most com-

monly used in current RFID applications.) Then, at the

warehouse loading zone, pallets are loaded into a truck,

and both the pallets and the truck are scanned by another

reader R2. The truck then departs to a retail store, through

a predeﬁned route (location L002). At the unloading zone

of the retail store (location L003), all pallets are unloaded

from the truck, and all cases are unpacked from the pallets.

All objects, the truck, pallets and cases, are scanned by an-

other reader R3, and then the cases are stocked in the store.

Eventually, when cases are purchased by customers (loca-

tion L004), they are scanned by reader R4 at the register.

In the whole process, observations from RFID readers are

made automatically, and data are also collected automati-

cally.

One key concept in the above RFID application is loca-

tion [18]. A location can be a geographic location or a sym-

bolic location. Here we assume symbolic location, which

can be a warehouse, a shipping route, a surgery room, or

a smart box [19]. Location changes come with the move-

ment of objects and business processes. Another key con-

cept is aggregation, such as containment. Containment de-

termines a hierarchical relationship among objects. For ex-

ample, a pallet is loaded with cases, or a toolbox contains

a set of tools [20]. The containment relationship implies

important logic information, for example, a truck leaving

a warehouse implies that all its contained objects leave the

warehouse too.

1129

Figure 1: A Sample RFID-Enabled Supply Chain System

We also observe that RFID data are highly dynamic, and

the data always carry state changes. On the other hand,

objects are associated with a unique ID encoded in EPC,

and these IDs are static and non-alterable.

Based on these observations, we propose a temporal-

oriented data model for RFID data, by ﬁrst identifying the

fundamental entities, as discussed next.

2.2 Fundamental Entities in RFID Systems

In an RFID system, there are two basic categories of data:

static data and dynamic data [21, 22]. Static data are re-

lated to commercial entities and product/service groups,

such as location information, product level and serial level

information. Dynamic data are speciﬁc to individual items.

There are two types of dynamic data: instance data such as

serial number and date of manufacture, and temporal data

such as observations, location and containment changes of

objects, which are all captured through EPC-tag readings.

Among all the data, the temporal data are directly related to

the fundamental business logic in RFID applications, such

as the movement and transaction of products, and are cru-

cial for an RFID data system to track and monitor objects.

By examining RFID systems, we summarize the follow-

ing primary entities that interact with each other and gen-

erate business processes.

–

Objects

. These include all EPC-tagged objects, such

as items, cases, pallets, trucks, even patients with

RFID-bracelets. For example, the objects in Figure

1(a) include cases, pallets, and trucks.

–

Sensors/Readers

. RFID readers use radio-frequency

signals to communicate with EPC tags and read the

EPC values. We have readers R1, R2, R3 and R4

(marked with circles) in Figure 1(a). Each RFID

reader (or its antenna) is also uniquely identiﬁed by

its EPC code. In this paper, we use “reader” and “sen-

sor” interchangeably.

–

Locations

. A location is symbolized to represent

where an object is/was. It can be a warehouse, a retail

store, a distribution center, or a route between two lo-

cations. For example, in Figure 1(a), the locations in-

clude: warehouse(L001), route from the warehouse to

a retail store(L002), retail store (L003) and customers

(L004). In a smart box application [19], locations are

simply “in-box” and “out-of-box”. The granularity

of locations can be deﬁned according to application

needs. A location is also associated with an owner.

–

Transactions

. There can be business transactions in

which EPC is involved. For example, a checkout in-

volves a credit card transaction with many EPC read-

ings. Transactions are business-speciﬁc, i.e., with dif-

ferent transaction types. Transactions are not consid-

ered in many RFID applications.

Next we discuss how these entities interact with each

other.

2.3 Dynamic Interactions between RFID Entities

While the above discussed entities are themselves static

in the business processes, they dynamically interact with

each other and generate movement, workﬂow, and busi-

ness logic. These interactions generate events and states

changes. State changes include:

–

Object location change

. For instance, the truck and its

loaded pallets leave the warehouse.

–

Object containment relationship change

. For example,

cases are packed onto pallets, as shown in Figure 1(a).

1130

–

Reader location change

. For instance, a reader is de-

ployed at a new location.

There are also another state change, ownership change,

for example, cases unloaded into the retail store. This can

be combined with location changes, since a location is al-

ways associated with an owner. The information about dur-

ing which period an object is in certain state is essential and

has to be captured.

Besides state changes, there are also events generated in

the interactions, including:

–

Observations

. These are generated when readers in-

teract with objects.

–

Transacted items

. These are generated when an object

participates into a transaction.

All events are associated with timestamps when they oc-

cur.

In a summary, the entities have dynamic relationships

with each other, which lead to events and states changes.

However, current RFID data management systems only

store and manage events, and state information is implicit

and has to be derived. This makes it very complicated to

search state information. For example, based on the event-

based model proposed in [22], it takes 8 steps for the query:

“where are the 5 CDs that were supposed to be in the last

order?”.

One of the essential goals of an RFID-enabled applica-

tion is to track objects and monitor the system at any lo-

cation, at any time, or both. This requires an expressive

data model that can explicitly represent the history of both

events and states, and capture fundamental business logic

into the data model itself, therefore complex queries such

as tracking and monitoring can be effectively supported.

In the following, we propose a Dynamic Relationship

ER Model (DRER) that ﬁts perfectly with RFID data.

2.4 Dynamic Relationship ER Model (DRER)

DRER is a temporal extension of ER model. In ER model,

all entities and relationships are static or current. In an

RFID system, entities are static, but all the relationships

are dynamic. Here we naturally extend ER model by inher-

iting all the ER model semantics and simply adding a new

relationship – dynamic relationship, as shown in Figure 2.

(For simplicity, other attributes are ignored in the ﬁgure.)

There are two types of dynamic relationships: relationship

that generates events, and relationship that generates state

history. Here we use dash lines to represent state-based dy-

namic relationship, and dash-dot lines to represent event-

based dynamic relationship.

Two attributes tstart and tend are associated with a

state-based dynamic relationship, to represent the lifespan

of that relationship; and an attribute timestamp is associ-

ated with an event-based dynamic relationship, to represent

the occurring timestamp of the event.

The simplicity of DRER is that, it requires minimum

extensions to ER model, by simply adding two new types

of relationships, each of which is associated with special

time-related attributes.

Thus, based on DRER data model, we have following

static entities for RFID data (Figure 2): SENSOR, OBJECT,

LOCATION, and TRANSACTION. State-based dynamic rela-

tionships include: SENSORLOCATION generated from SEN-

SOR and LOCATION, OBJECTLOCATION generated from

OBJECT and LOCATION, CONTAINMENT generated from

OBJECT and itself; Event-based dynamic relationships in-

clude: OBSERVATION generated from OBJECT and SEN-

SOR, and TRANSACTIONITEM generated from OBJECT and

TRANSACTION.

It is straightforward to implement DRER model in an

RDBMS. There are two types of mappings from the data

model to tables. Entities are mapped directly as entity ta-

bles. For a state-based dynamic relationship between two

entities, it is mapped as a table consisting of keys from both

entities, plus an interval [tstart, tend] to represent the

lifespan in which the relationship or the state exists. For an

event-based dynamic relationship, it is mapped as a table

consisting of keys from both entities, plus a timestamp to

represent the time when the event occurs. Normal (static)

relationships, if any, can be mapped as tables according to

common ER mapping rules.

We use symbol ‘UC’ to denote now. ‘UC’ can be rep-

resented as ‘end-of-time’, e.g., “9999-12-31 23:59:999”, in

the database. In the remainder of this paper, our granularity

for time is a millisecond, thus two continuous events have a

difference of 1 millisecond. All the techniques we present

are equally valid for any granularity used by an application.

The tables for DRER model are described as follows.

(Static) Entity Tables

SENSOR(sensor epc, name, description)

The SENSOR table records the EPC, name and descrip-

tion of a sensor (Table 1).

sensor epc name description

1.255.1 R1 Packing reader

1.255.2 R2 Departure reader

1.255.3 R3 Unloading reader

1.255.4 R4 Checkout reader

Table 1: Sample SENSOR

OBJECT(epc, name, description)

The OBJECT table includes the EPC, name, and de-

scription of an EPC-tagged object (Table 2).

epc name description

1.1.101 Case Containing items

1.1.102 Case Containing items

1.1.103 Case Containing items

1.1.104 Case Containing items

1.2.1 Pallet Containing cases

1.3.1 Truck Loaded with pallets

Table 2: Sample OBJECT

LOCATION(location id, name, owner)

Here we use a pseudo notation of EPC code for illustration

purpose. The format of EPC is deﬁned in [10].

1131

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